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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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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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IFGNN: An Individual Fairness Awareness Model for Missing Sensitive Information Graphs
Graph neural networks (GNNs) provide an approach for analyzing complicated graph data for node, edge, and graph-level prediction tasks. However, due...
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Discovering Densest Subgraph over Heterogeneous Information Networks
Densest Subgraph Discovery (DSD) is a fundamental and challenging problem in the field of graph mining in recent years. The DSD aims to determine,...
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Influence Maximization Revisited
Influence Maximization (IM) has been extensively studied, which is to select a set of k seed users from a social network to maximize the expected...
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Maximum Fairness-Aware (k, r)-Core Identification in Large Graphs
Cohesive subgraph mining is a fundamental problem in attributed graph analysis. The k-core model has been widely used in many studies to measure the...
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Projects
This is a special chapter dealing with security projects. We have arranged the projects in three parts. Part 1 consists of projects that can be done...
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Standardization and Security Criteria: Security Evaluation of Computer Products
Our growing dependence on technology and the corresponding skyrocketing security problems arising from it have all created a high demand for...
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Deep Learning-Based Solution for Intrusion Detection in the Internet of Things
Securing the Internet of Things-based environment is a top priority for consumers, businesses, and governments. There are billions of devices...
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Social Recommendation Using Deep Auto-encoder and Confidence Aware Sentiment Analysis
The development of online social networks has attracted increasing interest in social recommendation. On the other hand, recommender systems based on...