Traditional recommendation algorithms based on the user's historical behavior and popularity of the product, which generally have the problems of data sparsity and cold start. Knowledge graph technology can provid...
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In recent years, Face Anti-Spoofing (FAS) has played a crucial role in preserving the security of face recognition technology. With the rise of counterfeit face generation techniques, the challenge posed by digitally ...
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ISBN:
(数字)9798350365474
ISBN:
(纸本)9798350365481
In recent years, Face Anti-Spoofing (FAS) has played a crucial role in preserving the security of face recognition technology. With the rise of counterfeit face generation techniques, the challenge posed by digitally edited faces to face anti-spoofing is escalating. Existing FAS technologies primarily focus on intercepting physically forged faces and lack a robust solution for cross-domain FAS challenges. Moreover, determining an appropriate threshold to achieve optimal deployment results remains an issue for intra-domain FAS. To address these issues, we propose a visualization method that intuitively reflects the training outcomes of models by visualizing the prediction results on datasets. Additionally, we demonstrate that employing data augmentation techniques, such as downsampling and Gaussian blur, can effectively enhance performance on cross-domain tasks. Building upon our data visualization approach, we also introduce a methodology for setting threshold values based on the distribution of the training dataset. Ultimately, our methods secured us second place in both the Unified Physical-Digital Face Attack Detection competition and the Snapshot Spectral Imaging Face Anti-spoofing contest. The training code is available at https://***/SeaRecluse/CVPRW2024.
Multispectral image pairs can provide complementary visual information, making pedestrian detection systems more robust and reliable. To benefit from both RGB and thermal IR modalities, we introduce a novel attentive ...
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ISBN:
(纸本)9780738142661
Multispectral image pairs can provide complementary visual information, making pedestrian detection systems more robust and reliable. To benefit from both RGB and thermal IR modalities, we introduce a novel attentive multispectral feature fusion approach. Under the guidance of the inter- and intra-modality attention modules, our deep learning architecture learns to dynamically weigh and fuse the multispectral features. Experiments on two public multispectral object detection datasets demonstrate that the proposed approach significantly improves the detection accuracy at a low computation cost.
Contemporary single-cell technologies produce data with a vast number of variables at a rapid pace, making large volumes of high-dimensional data available. The exploratory analysis of such high dimensional data can b...
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ISBN:
(纸本)9781665401128
Contemporary single-cell technologies produce data with a vast number of variables at a rapid pace, making large volumes of high-dimensional data available. The exploratory analysis of such high dimensional data can be aided by intuitive low dimensional visualizations. In this work, we investigate how both discrete and continuous structures in single cell data can be captured using the recently proposed dimensionality reduction method SONG, and compare the results with commonly used methods UMAP and PHATE. Using simulated and real-world datasets, we observed that SONG preserves a variety of patterns including discrete clusters, continuums, and branching structures. More importantly, SONG produced more/equally insightful visualizations compared to UMAP and PHATE in all considered datasets. We also quantitatively validate the high-dimensional pairwise distance preservation ability of these visualization methods in the low dimensional space for the generated visualizations.
Observing topology and communication process in real time is critical for dealing with unexpected situations of Underwater Wireless Sensor Networks (UWSNs). However, due to low bandwidth, limited energy, and severe co...
Observing topology and communication process in real time is critical for dealing with unexpected situations of Underwater Wireless Sensor Networks (UWSNs). However, due to low bandwidth, limited energy, and severe collisions of underwater acoustic communication, UWSNs are unbearable to afford communication overhead of additional packets for visualization. Moreover, each packet only records the transmission information of one single hop, which imposes great challenges to match nodes with transmission results, failing to visualize the whole propagation path of one packet in multi-hop UWSNs. To this end, we propose a 3D data visualization system (TDVS) to visualize UWSNs’ topology and communication process in real time without extra communication overhead. TDVS consists of three parts: data collection, data process and UWSNs presentation. Data collection method is proposed to collect UWSNs’ status by overhearing packets on the broadcast acoustic channel, without additional energy consumption. Then we design a data process algorithm to extract and analyze visualization-related data from discrete and redundant packets, achieving sending and receiving match, success storage, error reproduction, and collision retrieval. UWSNs presentation module visualizes network topology, data propagation path, data content, and node movement with 3D models and animations. We verified stability and accuracy of TDVS in a NS-3 based simulator called Aqua-Sim-Tg and a 20-heterogeneous-node network sea trial in Dalian, China.
The automated manipulation of deformable linear objects (DLO) has been widely studied. Some of the typical tasks, i.e. plugging, insertion, routing, and obstacle avoidance can now be accomplished by robots in certain ...
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ISBN:
(数字)9781665483063
ISBN:
(纸本)9781665483063
The automated manipulation of deformable linear objects (DLO) has been widely studied. Some of the typical tasks, i.e. plugging, insertion, routing, and obstacle avoidance can now be accomplished by robots in certain conditions. In terms of cable harness assembly, which has variant categories and large number of tasks, assembly sequence of tasks has an impact on both assembly feasibility and assembly cost such as cycle time. However, a mathematical representation of cables for assembly sequence planning is not well formulated. This paper aims at establishing such a mathematical representation as the foundation of cable assembly sequence planning. We first give a mathematical definition of cable topological structure and assembly tasks as the foundation for establishing mathematical representation. Then, derivation algorithms are introduced to obtain both algebraical and graphical representations of a cable harness. The algorithms are later applied to typical examples for proof-of-concept.
Stereoencephalographic (SEEG) electrodes are clinically implanted into the brains of patients with refractory epilepsy to locate foci of seizure onset. They are increasingly used in neurophysiology research to determi...
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ISBN:
(纸本)9781728111797
Stereoencephalographic (SEEG) electrodes are clinically implanted into the brains of patients with refractory epilepsy to locate foci of seizure onset. They are increasingly used in neurophysiology research to determine focal human brain activity in response to tasks or stimuli. Clear visualization of SEEG electrode location with respect to patient anatomy on magnetic resonance image (MRI) scan is vital to neuroscientific understanding. An intuitive way to accomplish this is to plot brain activity and labels at electrode locations on closest MRI slices along the canonical axial, coronal, and sagittal planes. Therefore, we've developed an open-source software tool in Matlab for visualizing SEEG electrode positions, determined from computed tomography (CT), onto canonical planes of resliced brain MRI. The code and graphical user interface are available at:https://***/MultimodalNeuroimagingLab/mnl_seegview
Semantic communication plays a crucial role in human interactions, allowing for the exchange of complex ideas and concepts. In this paper, we introduce a novel approach to semantic communication leveraging image gener...
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ISBN:
(数字)9798331517786
ISBN:
(纸本)9798331517793
Semantic communication plays a crucial role in human interactions, allowing for the exchange of complex ideas and concepts. In this paper, we introduce a novel approach to semantic communication leveraging image generative Artificial Intelligence (AI) models, specifically stable diffusion models. Unlike conventional works, our system enables the transmission of images through a physical channel by transforming them into multi-dimensional semantic representations consisting of text descriptions, low-resolution sketches, and pose information. At the receiver’s end, these semantic representations are used to reconstruct the original image using a trained stable diffusion model. The benefits of our approach include reduced transmission bandwidth requirements, flexibility in reconstruction styles, adaptability to multiple receivers’ preferences, and the ability to omit unwanted image elements. We present preliminary results demonstrating the feasibility and effectiveness of our method. The similarity score between the transmitted images and reconstructed ones reach values ranging between 0.015 and 0.029 in Root Mean Square Error (RMSE) and between 0.993 and 0.998 using a Siamese network.
Virtual human avatars have long been studied in the field of embodied conversational agents. Most studies have focused on understanding how verbal and non-verbal cues build long-term relationships, trust, and credibil...
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ISBN:
(数字)9798350374490
ISBN:
(纸本)9798350374506
Virtual human avatars have long been studied in the field of embodied conversational agents. Most studies have focused on understanding how verbal and non-verbal cues build long-term relationships, trust, and credibility. Some studies have even gone as far as looking into characteristics like clothes, accessories, and race to see what effects they may have on the interlocutor. We seek feedback on an investigative study where we look to better understand how avatar race affects the perception of the avatar, information retention, and eye gaze behavior. We discuss the technical design and research methodology.
Urbanization, driven by technological advancements, has brought about improved connectivity and efficiency, especially with the rise of Internet of Things (IoT) devices. Smart cities use these innovations to manage re...
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ISBN:
(数字)9798350366259
ISBN:
(纸本)9798350366266
Urbanization, driven by technological advancements, has brought about improved connectivity and efficiency, especially with the rise of Internet of Things (IoT) devices. Smart cities use these innovations to manage resources better and enhance resident's quality of life. However, implementing smart city initiatives comes with challenges like monitoring, maintaining, and testing urban infrastructure. Digital Twin (DT) entails the connection of physical facilities or devices with their digital counterparts, facilitating real-time monitoring, manipulation, and predictive analysis of their behavior. This concept offers a virtual replica of assets, processes, and systems, enabling insights into their real-time performance and predictive behaviors. By simulating real-world scenarios, DT aids in planning maintenance activities and conducting comprehensive testing, thereby enhancing the resilience and efficiency of smart city systems. Particularly in the context of managing water networks, DT technology holds significant promise. visualization capabilities provide intuitive insights into the system's behavior, facilitating informed decision-making. This visualization, coupled with actuation capabilities, enables control actions based on predictive analytics and optimization algorithms, allowing for proactive management of water resources and infrastructure. To this end, in this paper, we present the architecture of WaterTwin, a DT developed for water quality networks in smart city systems. We demonstrate our approach through the use of a water quality network at the smart city living lab, IIIT Hyderabad campus.
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