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Multimodal Information Retrieval
In today’s rapidly evolving digital landscape, the wealth of available information has expanded beyond the boundaries of traditional text-based...
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Outlook
While multimodal information retrieval has several exciting applications and a high potential for impact on important problems, there are several...
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Multimodal Content Generation
In this chapter, we will review the advances that are being made in this new field of multimodal content generation and also discuss several...
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Retrieval Augmented Modeling
Till this point in our book, we have discussed the fundamental principles of information retrieval, exploring its key elements, and various...
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Transformer-Driven Models for Language, Vision, and Multimodality
In this chapter, we will learn about the modeling and learning techniques that drive multimodal applications. We will focus specifically on the...
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Introduction
In this book, our emphasis is on multimodal information retrieval, specifically concentrating on text and image data. The traditional unimodal...
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Smell and Taste-Based Interactions Enabled Through Advances in Digital Technology
Innovations around smell and taste interfaces are quickly emerging in the literature and practice, they include fully controllable sensory delivery...
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From Databases to Exchange Formats
This chapter addresses the question of how to transform relational database content into exchange formats in order to enable data interoperability...
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Process Orchestration: Conceptual Design
This chapter explains and discusses concepts for the conceptual design and formal verification of process orchestrations, employing notations such as...
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kNN Join for Dynamic High-Dimensional Data: A Parallel Approach
The k nearest neighbor (kNN) join operation is a fundamental task that combines two high-dimensional databases, enabling data points in the User...
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Multi-level Storage Optimization for Intermediate Data in AI Model Training
As Transformer-based large models become the mainstream of AI training, the development of hardware devices (e.g., GPUs) cannot keep up with the...
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Take a Close Look at the Optimization of Deep Kernels for Non-parametric Two-Sample Tests
The maximum mean discrepancy (MMD) test with deep kernel is a powerful method to distinguish whether two samples are drawn from the same...
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Balanced Hop-Constrained Path Enumeration in Signed Directed Graphs
Hop-constrained path enumeration, which aims to output all the paths from two distinct vertices within the given hops, is one of the fundamental...
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Probabilistic Reverse Top-k Query on Probabilistic Data
Reverse top-k queries have received much attention from research communities. The result of reverse top-k queries is a set of objects, which had the...
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Device Characterization for Opportunistic Networks
When implementing opportunistic networks in real applications, not only connectivity problems need to be addressed, but also device restrictions....
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Simulation Tools for Opportunistic Networks
In this chapter, we start with an introduction to network simulators and especially to discrete event simulators. We explain how they work and what...
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Security in Opportunistic Networks
Security is of paramount importance in Opportunistic Networks (OppNets) due to its unique characteristics and operational challenges. OppNets are...
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Connectivity Technologies for Opportunistic Networks
This chapter explores the question of how to implement opportunistic networks in practice. The main part of this question considers the connectivity...
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Theoretical Models for Opportunistic Networks
In this chapter, we detail how to use theoretical modeling in opportunistic networks (OppNets) using mathematical and computational methods to...
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Data Dissemination in Opportunistic Networks
Data dissemination is the main research and implementation challenge in opportunistic networks. It addresses the question of how to forward data in a...