259 research outputs found
Routing Protocols for Underwater Acoustic Sensor Networks: A Survey from an Application Perspective
Underwater acoustic communications are different from terrestrial radio communications; acoustic channel is asymmetric and has large and variable end‐to‐end propagation delays, distance‐dependent limited bandwidth, high bit error rates, and multi‐path fading. Besides, nodes’ mobility and limited battery power also cause problems for networking protocol design. Among them, routing in underwater acoustic networks is a challenging task, and many protocols have been proposed. In this chapter, we first classify the routing protocols according to application scenarios, which are classified according to the number of sinks that an underwater acoustic sensor network (UASN) may use, namely single‐sink, multi‐sink, and no‐sink. We review some typical routing strategies proposed for these application scenarios, such as cross‐layer and reinforcement learning as well as opportunistic routing. Finally, some remaining key issues are highlighted
Masked Vision-Language Transformers for Scene Text Recognition
Scene text recognition (STR) enables computers to recognize and read the text
in various real-world scenes. Recent STR models benefit from taking linguistic
information in addition to visual cues into consideration. We propose a novel
Masked Vision-Language Transformers (MVLT) to capture both the explicit and the
implicit linguistic information. Our encoder is a Vision Transformer, and our
decoder is a multi-modal Transformer. MVLT is trained in two stages: in the
first stage, we design a STR-tailored pretraining method based on a masking
strategy; in the second stage, we fine-tune our model and adopt an iterative
correction method to improve the performance. MVLT attains superior results
compared to state-of-the-art STR models on several benchmarks. Our code and
model are available at https://github.com/onealwj/MVLT.Comment: The paper is accepted by the 33rd British Machine Vision Conference
(BMVC 2022
Bring Me a Good One: Seeking High-potential Startups using Heterogeneous Venture Information Networks
Identifying startups with the highest potential for success is a complex task, necessitating the examination of various information sources, including firm demographics, management team composition, and financial performance. The effectiveness of existing methodologies, such as feature-based and network-topological approaches, is limited for predicting high-potential startups. In response, we propose a novel Venture Graph Neural Network (VenGNN) model, leveraging Heterogeneous Information Networks (HIN) and Graph Neural Networks (GNN) techniques to address the prediction problem. Specifically, we construct a Heterogeneous Venture Information Network (HVIN) using raw business data and deem the prediction a node classification task. Our model integrates theory-guided semantic meta-paths, firm demographics, sampling-based self-attention, and centrality encoding to overcome certain constraints of existing GNNs. Our experimental analysis reveals that VenGNN outperforms state-of-the-art models by 15-20% across a wide range of performance metrics. Our study also includes a comprehensive interpretation analysis to provide investors with an essential understanding for better decision-making
Bring Me a Good One: Seeking High-potential Startups using Heterogeneous Venture Information Networks
The rapid acceleration of technology and the evolving global economy have led to a significant surge in high-potential startups, presenting immense opportunities for venture capital firms and investors to support and benefit from these innovative ventures. However, identifying startups with the highest likelihood of success remains a complex task, necessitating the examination of various information sources, including firm demographics, management team composition, and financial performance. The effectiveness of existing methodologies, such as feature-based and network-topological approaches, is limited for predicting high-potential startups. In response, we propose a novel Venture Graph Neural Network (VenGNN) model, leveraging Heterogeneous Information Networks (HIN) and Graph Neural Networks (GNN) techniques to address the prediction problem. Our experimental analysis reveals that VenGNN outperforms state-of-the-art models by 15-20% across a wide range of performance metrics
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