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Found 14 papers, 3 papers with code
Channel Estimation, Interpolation and Extrapolation in Doubly-dispersive Channels
Both aliasing and ISCI are considered in channel modelling, a low-complexity algorithm is proposed for channel estimation and interpolation through FFT.
ContextBLIP: Doubly Contextual Alignment for Contrastive Image Retrieval from Linguistically Complex Descriptions
The main challenges lie in aligning key contextual cues in two modalities, where these subtle cues are concealed in tiny areas of multiple contrastive images and within the complex linguistics of textual descriptions.
Uncovering What, Why and How: A Comprehensive Benchmark for Causation Understanding of Video Anomaly
In pursuit of these answers, we present a comprehensive benchmark for Causation Understanding of Video Anomaly (CUVA).
Adaptive Federated Learning in Heterogeneous Wireless Networks with Independent Sampling
In this work, we advocate a new independent client sampling strategy to minimize the wall-clock training time of FL, while considering data heterogeneity and system heterogeneity in both communication and computation.
Can We Improve Channel Reciprocity via Loop-back Compensation for RIS-assisted Physical Layer Key Generation
The results demonstrate a significant improvement in both the rate of key generation assisted by the RIS and the consistency of the generated keys, showing great potential for the practical deployment of our LoCKey in future wireless systems.
Uncovering What Why and How: A Comprehensive Benchmark for Causation Understanding of Video Anomaly
In addition we also introduce MMEval a novel evaluation metric designed to better align with human preferences for CUVA facilitating the measurement of existing LLMs in comprehending the underlying cause and corresponding effect of video anomalies.
Refining Latent Homophilic Structures over Heterophilic Graphs for Robust Graph Convolution Networks
This finding motivates us to present a novel method that aims to harden GCNs by automatically learning Latent Homophilic Structures over heterophilic graphs.
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DocMSU: A Comprehensive Benchmark for Document-level Multimodal Sarcasm Understanding
Multimodal Sarcasm Understanding (MSU) has a wide range of applications in the news field such as public opinion analysis and forgery detection.
FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participants
Federated learning (FL) provides a privacy-preserving approach for collaborative training of machine learning models.
Passive Eavesdropping Can Significantly Slow Down RIS-Assisted Secret Key Generation
Reconfigurable Intelligent Surface (RIS) assisted physical layer key generation has shown great potential to secure wireless communications by smartly controlling signals such as phase and amplitude.
3D-IDS: Doubly Disentangled Dynamic Intrusion Detection
This motivates us to propose 3D-IDS, a novel method that aims to tackle the above issues through two-step feature disentanglements and a dynamic graph diffusion scheme.
Physical-layer Adversarial Robustness for Deep Learning-based Semantic Communications
We argue that central to the success of ESC is the robust interpretation of conveyed semantics at the receiver side, especially for security-critical applications such as automatic driving and smart healthcare.
Boosting Physical Layer Black-Box Attacks with Semantic Adversaries in Semantic Communications
To this end, we present SemBLK, a novel method that can learn to generate destructive physical layer semantic attacks for an ESC system under the black-box setting, where the adversaries are imperceptible to humans.
Similarity-based Label Inference Attack against Training and Inference of Split Learning
We mathematically analyze the potential label leakages and propose the cosine and Euclidean similarity measurements for gradients and smashed data, respectively.
