159 research outputs found
MeMaHand: Exploiting Mesh-Mano Interaction for Single Image Two-Hand Reconstruction
Existing methods proposed for hand reconstruction tasks usually parameterize
a generic 3D hand model or predict hand mesh positions directly. The parametric
representations consisting of hand shapes and rotational poses are more stable,
while the non-parametric methods can predict more accurate mesh positions. In
this paper, we propose to reconstruct meshes and estimate MANO parameters of
two hands from a single RGB image simultaneously to utilize the merits of two
kinds of hand representations. To fulfill this target, we propose novel
Mesh-Mano interaction blocks (MMIBs), which take mesh vertices positions and
MANO parameters as two kinds of query tokens. MMIB consists of one graph
residual block to aggregate local information and two transformer encoders to
model long-range dependencies. The transformer encoders are equipped with
different asymmetric attention masks to model the intra-hand and inter-hand
attention, respectively. Moreover, we introduce the mesh alignment refinement
module to further enhance the mesh-image alignment. Extensive experiments on
the InterHand2.6M benchmark demonstrate promising results over the
state-of-the-art hand reconstruction methods
Evolve Path Tracer: Early Detection of Malicious Addresses in Cryptocurrency
With the ever-increasing boom of Cryptocurrency, detecting fraudulent
behaviors and associated malicious addresses draws significant research effort.
However, most existing studies still rely on the full history features or
full-fledged address transaction networks, thus cannot meet the requirements of
early malicious address detection, which is urgent but seldom discussed by
existing studies. To detect fraud behaviors of malicious addresses in the early
stage, we present Evolve Path Tracer, which consists of Evolve Path Encoder
LSTM, Evolve Path Graph GCN, and Hierarchical Survival Predictor. Specifically,
in addition to the general address features, we propose asset transfer paths
and corresponding path graphs to characterize early transaction patterns.
Further, since the transaction patterns are changing rapidly during the early
stage, we propose Evolve Path Encoder LSTM and Evolve Path Graph GCN to encode
asset transfer path and path graph under an evolving structure setting.
Hierarchical Survival Predictor then predicts addresses' labels with nice
scalability and faster prediction speed. We investigate the effectiveness and
versatility of Evolve Path Tracer on three real-world illicit bitcoin datasets.
Our experimental results demonstrate that Evolve Path Tracer outperforms the
state-of-the-art methods. Extensive scalability experiments demonstrate the
model's adaptivity under a dynamic prediction setting.Comment: In Proceedings of the 29th ACM SIGKDD Conference on Knowledge
Discovery and Data Mining (KDD23
Code Will Tell: Visual Identification of Ponzi Schemes on Ethereum
Ethereum has become a popular blockchain with smart contracts for investors
nowadays. Due to the decentralization and anonymity of Ethereum, Ponzi schemes
have been easily deployed and caused significant losses to investors. However,
there are still no explainable and effective methods to help investors easily
identify Ponzi schemes and validate whether a smart contract is actually a
Ponzi scheme. To fill the research gap, we propose PonziLens, a novel
visualization approach to help investors achieve early identification of Ponzi
schemes by investigating the operation codes of smart contracts. Specifically,
we conduct symbolic execution of opcode and extract the control flow for
investing and rewarding with critical opcode instructions. Then, an intuitive
directed-graph based visualization is proposed to display the investing and
rewarding flows and the crucial execution paths, enabling easy identification
of Ponzi schemes on Ethereum. Two usage scenarios involving both Ponzi and
non-Ponzi schemes demonstrate the effectiveness of PonziLens
- …