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Works148 from public data
- Learning Intents behind Interactions with Knowledge Graph for Recommendation638
This study explores intents behind a user-item interaction by using auxiliary item knowledge, and proposes a new model, Knowledge Graph-based Intent Network (KGIN), which achieves significant improvements over the state-of-the-art methods like KGAT, KGNN-LS, and CKAN.
- NAIS: Neural Attentive Item Similarity Model for Recommendation593
This work proposes a neural network model named Neural Attentive Item Similarity model (NAIS), which is the first attempt that designs neural network models for item-based CF, opening up new research possibilities for future developments of neural recommender systems.
- Smart Contract Vulnerability Detection using Graph Neural Network475
This paper constructs a contract graph to represent both syntactic and semantic structures of a smart contract function, and proposes a degree-free graph convolutional neural network (DR-GCN) and a novel temporal message propagation network (TMP) to learn from the normalized graphs for vulnerability detection.
- LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting244
The LargeST benchmark dataset is introduced, which encompasses a total number of 8,600 sensors in California with a 5-year time coverage and includes comprehensive metadata and performs in-depth data analysis to extract data insights, benchmark well-known baselines in terms of their performance and efficiency, and identify challenges as well as opportunities for future research.
- Addressing Heterophily in Graph Anomaly Detection: A Perspective of Graph Spectrum242
The proposed indicator can effectively reduce the heterophily degree of the graph, thus boosting the overall GAD performance and showing that prediction errors are less likely to affect the identification process.
- Towards Automated Reentrancy Detection for Smart Contracts Based on Sequential Models228
This work attempts to utilize the deep learning-based approach, namely bidirectional long-short term memory with attention mechanism (BLSTM-ATT), aiming to precisely detect reentrancy bugs, and proposes contract snippet representations for smart contracts, which contributes to capturing essential semantic information and control flow dependencies.
- Smart Contract Vulnerability Detection: From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusion178
This paper develops automatic tools to extract expert patterns from the source code and casts the code into a semantic graph to extract deep graph features and significantly outperforms state-of-the-art methods.
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- Deep Dual Consecutive Network for Human Pose Estimation144
A novel multi-frame human pose estimation framework, leveraging abundant temporal cues between video frames to facilitate keypoint detection is proposed, which ranks No.1 in the Multi-frame Person Pose Estimation Challenge on the large-scale benchmark datasets PoseTrack2017 and PoseTrack2018.
- Rethinking Smart Contract Fuzzing: Fuzzing With Invocation Ordering and Important Branch Revisiting137
This paper implements IR-Fuzz, a fuzzing technique for smart contracts that detects more vulnerabilities and increases the average accuracy of vulnerability detection by 7% over current methods, and is fast, generating an average of 350 test cases per second.
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- Exposing the Deception: Uncovering More Forgery Clues for Deepfake Detection123
A novel framework to capture broader forgery clues by extracting multiple non-overlapping local representations and fusing them into a global semantic-rich feature, and derives Local Information Loss to guarantee the orthogonality of local representations while preserving comprehensive task-relevant information.
- Cross-Modality Mutual Learning for Enhancing Smart Contract Vulnerability Detection on Bytecode111
A novel cross-modality mutual learning framework for enhancing smart contract vulnerability detection on bytecode and Experimental results show that this method significantly surpasses state-of-the-art approaches.
- 2D Human pose estimation: a survey111
This paper seeks to provide researchers with a more comprehensive and systematic review on human pose estimation, allowing them to acquire a grand panorama and better identify future directions.
- Alleviating Structural Distribution Shift in Graph Anomaly Detection106
A prototype vector is devised to infer and update this distribution of anomaly features during training to mitigate the effect of heterophilous neighbors and make them invariant, and the proposed framework achieves a remarkable performance boost in GAD.
- Investigating Pose Representations and Motion Contexts Modeling for 3D Motion Prediction96
A novel RNN architecture termed AHMR (Attentive Hierarchical Motion Recurrent network) for motion prediction which simultaneously models local motion contexts and a global context is proposed and achieves much enhanced long-term prediction proficiency, such as retaining natural human-like motions over 50 seconds predictions.
- Behavior pattern clustering in blockchain networks96
This paper introduces the problem of behavior pattern clustering in blockchain networks and proposes a novel algorithm termed BPC for this problem, evaluating a long list of potential sequence similarity measures, and selecting a distance that is suitable for the behaviorpattern clustering problem.
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- Blockchain for finance: A survey82
The authors focus on blockchain‐based securities trading, in which blockchain technology plays a vital role in financial services as it ultimately lifts trust and frees the need for third‐party verification by using consensus‐based verification.
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- Temporal Feature Alignment and Mutual Information Maximization for Video-Based Human Pose Estimation78
A novel hierarchical alignment framework is presented, which leverages coarse-to-fine deformations to progressively update a neighboring frame to align with the current frame at the feature level and theoretically analyzed the mutual information between the frames and arrived at a loss that maximizes the task-relevant mutual information.
- Locate and Verify: A Two-Stream Network for Improved Deepfake Detection73
An innovative two-stream network is proposed that effectively enlarges the potential regions from which the model extracts forgery evidence, and a Semi-supervised Patch Similarity Learning strategy to estimate patch-level forged location annotations is proposed.
- Aggregated Multi-GANs for Controlled 3D Human Motion Prediction66
A generalization of the human motion prediction task in which control parameters can be readily incorporated to adjust the forecasted motion, and a simple yet effective composite GAN structure, consisting of local GANs for different body parts and aggregated via a global GAN is presented.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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