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检索条件"丛书名=Lecture Notes in Electrical Engineering"

共 22048 条记录，以下是1-10 订阅

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Intelligent Control and Innovative Computing (lecture notes in electrical engineering) 1

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丛书名： lecture notes in electrical engineering

2012年

作者： Sio Iong Ao, Oscar Castillo, Xu Huang

ISBN: (数字)9781461416951

ISBN: (纸本)1461416949

A large international conference on Advances in Intelligent Control and Innovative Computing was held in Hong Kong, March March 16-18, 2011, under the auspices of the International MultiConference of Engineers and Computer Scientists (IMECS 2010). The IMECS is organized by the International Association of Engineers (IAENG). Intelligent Control and Computer engineering contains 25 revised and extended research articles written by prominent researchers participating in the conference. Topics covered include artificial intelligence, control engineering, decision supporting systems, automated planning, automation systems, systems identification, modelling and simulation, communication systems, signal processing, and industrial applications. Intelligent Control and Innovative Computing offers the state of the art of tremendous advances in intelligent control and computer engineering and also serves as an excellent reference text for researchers and graduate students, working on intelligent control and computer engineering.

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Electronics System Design Techniques for Safety Critical Applications (lecture notes in electrical engineering) 1

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丛书名： lecture notes in electrical engineering

2008年

作者： Luca Sterpone

ISBN: (纸本)1402089783;9781402089787;9781402089794;2008934322

The main purpose of the present manuscript addresses the development of techniques for the evaluation and the hardening of designs implemented on SRAM-based Field Programmable Gate Arrays against the radiation induced effects such as Single Event Upsets (SEUs) or Soft-Errors (SEs). The perspective of the analysis and the design flows proposed in this manuscript are aimed at defining a novel and complete design methodology solving the industrial designers needs for implementing electronic systems using SRAM-based FPGAs in critical environments, like the space or avionic ones. The main contribution of the proposed manuscript consists in a new reliability-oriented place and route algorithm that, coupled with Triple Modular Redundancy (TMR), is able to effectively mitigate the effects of radiation in SRAM-based FPGA devices. The manuscript offers also the analysis of several fields where the usage of reconfigurable logic devices introduces several advantages such as the reconfigurable computing for multimedia applications and biomedical applications.

关键词： Field programmable gate arrays Electronic systems

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Green Communications and Networks: Proceedings of the International Conference on Green Communications and Networks (GCN 2011) (lecture notes in electrical engineering) 1

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丛书名： lecture notes in electrical engineering

2012年

作者： Xin-She Yang, Maode Ma

ISBN: (数字)9789400721692

ISBN: (纸本)9400721684

The objective of GCN 2011 is to facilitate an exchange of information on best practices for the latest research advances in the area of green communications and networks, which mainly includes the intelligent control, or efficient management, or optimal design of access network infrastructures, home networks, terminal equipment, and etc. Topics of interests include network design methodology, enabling technologies, network components and devices, applications, others and emerging new topics.

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Diversify and Conquer: Bandits and Diversity for an Enhanced E-commerce Homepage Experience 5th

Diversify and Conquer: Bandits and Diversity for an Enhanced...

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5th Workshop on Recommender Systems Conference

作者： Jaiswal, Sangeet Malayil, Korah T. Jawaid, Saif Vempati, Sreekanth Myntra Designs Pvt Ltd Bangalore Karnataka India

ISBN: (纸本)9783031768804;9783031768781;9783031768774

In the realm of e-commerce, popular platforms utilize widgets to recommend advertisements and products to their users. However, the prevalence of mobile device usage on these platforms introduces a unique challenge due to the limited screen real estate available. Consequently, the positioning of relevant widgets becomes pivotal in capturing and maintaining customer engagement. Given the restricted screen size of mobile devices, widgets placed at the top of the interface are more prominently displayed and thus attract greater user attention. Conversely, widgets positioned further down the page require users to scroll, reducing visibility and subsequent lower impression rates. Therefore it becomes imperative to place relevant widgets on top. However, selecting relevant widgets to display is a challenging task as the widgets can be heterogeneous, widgets can be introduced or removed at any given time from the platform. In this work, we model the vertical widget reordering as a contextual multi-arm bandit problem with delayed batch feedback. The objective is to rank the vertical widgets in a personalized manner. We present a two-stage ranking framework that combines contextual bandits with a diversity layer to improve the overall ranking. We demonstrate its effectiveness through offline and online A/B results, conducted on proprietary data from Myntra, a major fashion e-commerce platform in India.

关键词： Marketplaces

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AdBooster: Personalized Ad Creative Generation Using Stable Diffusion Outpainting 5th

AdBooster: Personalized Ad Creative Generation Using Stable ...

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5th Workshop on Recommender Systems Conference

作者： Shilova, Veronika Dos Santos, Ludovic Vasile, Flavian Racic, Gaetan Tanielian, Ugo Criteo 32 Rue Blanche F-75009 Paris France

ISBN: (纸本)9783031768804;9783031768781;9783031768774

In digital advertising, the selection of the optimal item (recommendation) and its best creative presentation (creative optimization) have traditionally been considered separate disciplines. However, both contribute significantly to user satisfaction, underpinning our assumption that it relies on both an item's relevance and its presentation, particularly in the case of visual creatives. In response, we introduce the task of Generative Creative Optimization (GCO), which proposes the use of generative models for creative generation that incorporate user interests, and AdBooster, a model for personalized ad creatives based on the Stable Diffusion outpainting architecture. This model uniquely incorporates user interests both during fine-tuning and at generation time. To further improve AdBooster's performance, we also introduce an automated data augmentation pipeline. Through our experiments on simulated data, we validate AdBooster's effectiveness in generating more relevant creatives than default product images, showing its potential of enhancing user engagement.

关键词： Generative adversarial networks

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Tailor: Size Recommendations for High-End Fashion Marketplaces 5th

Tailor: Size Recommendations for High-End Fashion Marketplac...

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5th Workshop on Recommender Systems Conference

作者： Candeias, Alexandre Silva, Ivo Sousa, Vitor Marcelino, Jose Farfetch Lisbon Portugal

ISBN: (纸本)9783031768804;9783031768781;9783031768774

In the ever-changing and dynamic realm of high-end fashion marketplaces, providing accurate and personalized size recommendations has become a critical aspect. Meeting customer expectations in this regard is not only crucial for ensuring their satisfaction but also plays a pivotal role in driving customer retention, which is a key metric for the success of any fashion retailer. We propose a novel sequence classification approach to address this problem, integrating implicit (Add2Bag) and explicit (ReturnReason) user signals. Our approach comprises two distinct models: one employs LSTMs to encode the user signals, while the other leverages an Attention mechanism. Our best model outperforms SFNet, improving accuracy by 45.7%. By using Add2Bag interactions we increase the user coverage by 24.5% when compared with only using Orders. Moreover, we evaluate the models' usability in real-time recommendation scenarios by conducting experiments to measure their latency performance.

关键词： Marketplaces

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Automated Material Properties Extraction For Enhanced Beauty Product Discovery and Makeup Virtual Try-on 5th

Automated Material Properties Extraction For Enhanced Beauty...

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5th Workshop on Recommender Systems Conference

作者： Dezaki, Fatemeh Taheri Arora, Himanshu Suresh, Rahul Banitalebi-Dehkordi, Amin Celikik, Marjan Amazon BeautyTech Vancouver BC Canada

ISBN: (纸本)9783031768804;9783031768781;9783031768774

The multitude of makeup products available can make it challenging to find the ideal match for desired attributes. An intelligent approach for product discovery is required to enhance the makeup shopping experience to make it more convenient and satisfying. However, enabling accurate and efficient product discovery requires extracting detailed attributes like color and finish type. Our work introduces an automated pipeline that utilizes multiple customized machine learning models to extract essential material attributes from makeup product images. Our pipeline is versatile and capable of handling various makeup products. To showcase the efficacy of our pipeline, we conduct extensive experiments on eyeshadow products (both single and multi-shade ones), a challenging makeup product known for its diverse range of shapes, colors, and finish types. Furthermore, we demonstrate the applicability of our approach by successfully extending it to other makeup categories like lipstick and foundation, showcasing its adaptability and effectiveness across different beauty products. Additionally, we conduct ablation experiments to demonstrate the superiority of our machine learning pipeline over human labeling methods in terms of reliability. Our proposed method showcases its effectiveness in cross-category product discovery, specifically in recommending makeup products that perfectly match a specified outfit. Lastly, we also demonstrate the application of these material attributes in enabling virtual-try-on experiences which makes makeup shopping experience significantly more engaging.

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Exploring Fit and Shape for Predicting Garment Size Issues in Fashion with Multi-task Learning 5th

Exploring Fit and Shape for Predicting Garment Size Issues i...

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5th Workshop on Recommender Systems Conference

作者： Mbarek, Sahar Lefakis, Leonidas Gehler, Peter Aurelio, Sonia Weffer, Rodrigo Shirvany, Reza Zalando SE Tamara Danz Str 1 D-10243 Berlin Germany

ISBN: (纸本)9783031768804;9783031768781;9783031768774

Online shoppers face many difficulties finding the right garment size that fits them. There has been a large body of work that try to tackle this problem from a customer perspective. Most often the approaches rely solely on purchases and returns to offer a personalized size-recommendation. In this work we investigate the potential effect of additional fashion characteristics such as shape and fit attributes of an article to better predict a potential size issue even before a purchase has been made. We frame the problem as a Multi-Task-Learning (MTL) problem and extend prior work to predict size issues from garments visual information. In the experiments, we explore different MTL architectures and a variety of datasets to further harness the power of these models. To the best of our knowledge, the proposed approach is the first to combine size, fit and shape attributes of fashion articles to address the challenge of size issue prediction in online shopping. Our findings indicate that there is a positive effect of additional attributes and that we can improve the size-issue prediction in comparison to the state-of-the-art models.

关键词： Multi-task learning

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UNICON: A Unified Framework for Behavior-Based Consumer Segmentation in E-Commerce 5th

UNICON: A Unified Framework for Behavior-Based Consumer Segm...

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5th Workshop on Recommender Systems Conference

作者： Dibak, Manuel Vlasov, Vladimir Karessli, Nour Dedik, Darya Malykh, Egor Wasilewski, Jacek Torres, Ton Ramallo, Ana Peleteiro Deldjoo, Yashar Zalando SE Berlin Germany

ISBN: (纸本)9783031768804;9783031768781;9783031768774

Data-driven personalization is a key practice in fashion e-commerce, improving the way businesses serve their consumers' needs with more relevant content. While hyper-personalization offers highly targeted experiences to each consumer, it requires a significant amount of private data to create an individualized journey. To alleviate this, group-based personalization provides a moderate level of personalization built on broader common preferences of a consumer segment, while still being able to personalize the results. We introduce UNICON, a unified deep learning consumer segmentation framework that leverages rich consumer behavior data to learn long-term latent representations and utilizes them to extract two pivotal types of segmentation catering various personalization use-cases: lookalike, expanding a predefined target seed segment with consumers of similar behavior, and data-driven, revealing non-obvious consumer segments with similar affinities. We demonstrate through extensive experimentation our framework's effectiveness in fashion to identify lookalike Designer audience and data-driven style segments. Furthermore, we present experiments that showcase how segment information can be incorporated in a hybrid recommender system combining hyper and group-based personalization to exploit the advantages of both alternatives and provide improvements on consumer experience.

关键词： Marketplaces

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Efficient Large-Scale Visual Representation Learning and Evaluation 5th

Efficient Large-Scale Visual Representation Learning and Eva...

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5th Workshop on Recommender Systems Conference

作者： Dolev, Eden Awad, Alaa Roberts, Denisa Olteanu Ebrahimzadeh, Zahra Mejran, Marcin Malpani, Vaibhav Yavuz, Mahir Etsy 117 Adams St Brooklyn NY USA 154W 15th St New York NY 10011 USA Etsy Brooklyn NY USA

ISBN: (纸本)9783031768804;9783031768781;9783031768774

Efficiently learning visual representations of items is vital for large-scale fashion recommendations in e-commerce. In this article we compare several pre-trained efficient backbone architectures, both in the convolutional neural network (CNN) and in the vision transformer (ViT) family. We describe challenges in e-commerce vision applications at scale and highlight methods to efficiently train, evaluate, and serve visual representations. We present ablation studies that evaluate visual representations in several downstream tasks. To this end, we present a novel multilingual text-to-image generative offline evaluation method for visually similar fashion recommendation systems. Finally, we include online results from machine learning systems deployed in production on a large-scale e-commerce platform.

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