Frontiers of Computer Science最新文献

Clustering is widely exploited in data mining. It has been proved that embedding weak label prior into clustering is effective to promote its performance. Previous researches mainly focus on only one type of prior. However, in many real scenarios, two kinds of weak label prior information, e.g., pairwise constraints and cluster ratio, are easily obtained or already available. How to incorporate them to improve clustering performance is important but rarely studied. We propose a novel constrained Clustering with Weak Label Prior method (CWLP), which is an integrated framework. Within the unified spectral clustering model, the pairwise constraints are employed as a regularizer in spectral embedding and label proportion is added as a constraint in spectral rotation. To approximate a variant of the embedding matrix more precisely, we replace a cluster indicator matrix with its scaled version. Instead of fixing an initial similarity matrix, we propose a new similarity matrix that is more suitable for deriving clustering results. Except for the theoretical convergence and computational complexity analyses, we validate the effectiveness of CWLP through several benchmark datasets, together with its ability to discriminate suspected breast cancer patients from healthy controls. The experimental evaluation illustrates the superiority of our proposed approach.

We aim to protect text generation APIs in this work. Previous LW methods compromised text quality and made watermarks easy to detect through error analysis due to not considering polysemy. To fit this, we propose meaning-preserving lexical substitution method that considers the target word’s correct meaning in context x. This enables high-confidence identification while making watermarks more invisible.

In this paper, we formulate the program retrieval problem as a graph similarity problem. This is achieved by first explicitly representing queries and program snippets as AMR and CPG, respectively. Then, through intra-level and inter-level attention mechanisms to infer fine-grained correspondence by propagating node correspondence along the graph edge. Moreover, such a design can learn correspondence of nodes at different levels, which were mostly ignored by previous works. Experiments have demonstrated the superiority of USRAE.

After the Ethereum DAO attack in 2016, which resulted in significant economic losses, blockchain governance has become a prominent research area. However, there is a lack of comprehensive and systematic literature review on blockchain governance. To deeply understand the process of blockchain governance and provide guidance for the future design of the blockchain governance model, we provide an in-depth review of blockchain governance. In this paper, first we introduce the consensus algorithms currently used in blockchain and relate them to governance theory. Second, we present the main content of off-chain governance and investigate two well-known off-chain governance projects. Third, we investigate four common on-chain governance voting techniques, then summarize the seven attributes that the on-chain governance voting process should meet, and finally analyze four well-known on-chain governance blockchain projects based on the previous research. We hope this survey will provide an in-depth insight into the potential development direction of blockchain governance and device future research agenda.

With the emergence of network-centric data, social network graph publishing is conducive to data analysts to mine the value of social networks, analyze the social behavior of individuals or groups, implement personalized recommendations, and so on. However, published social network graphs are often subject to re-identification attacks from adversaries, which results in the leakage of users’ privacy. The k-anonymity technology is widely used in the field of graph publishing, which is quite effective to resist re-identification attacks. However, the current researches still exist some issues to be solved: the protection of directed graphs is less concerned than that of undirected graphs; the protection of graph structure is often ignored while achieving the protection of nodes’ identities; the same protection is performed for different users, which doesn’t meet the different privacy requirements of users. Therefore, to address the above issues, a multi-level k-degree anonymity (MLDA) scheme on directed social network graphs is proposed in this paper. First, node sets with different importance are divided by the firefly algorithm and constrained connectedness upper approximation, and they are performed different k-degree anonymity protection to meet the different privacy requirements of users. Second, a new graph anonymity method is proposed, which achieves the addition and removal of edges with the help of fake nodes. In addition, to improve the utility of the anonymized graph, a new edge cost criterion is proposed, which is used to select the most appropriate edge to be removed. Third, to protect the community structure of the original graph as much as possible, fake nodes contained in a same community are merged prior to fake nodes contained in different communities. Experimental results on real datasets show that the newly proposed MLDA scheme is effective to balance the privacy and utility of the anonymized graph.

Comprehensive comparison results on the above two datasets indicate that the detection improvements proposed in CWYOLO framework for low-light conditions are effective and can stand out among the existing excellent method. In future work, we would explore a more efficient and lightweight network architecture with group convolution to advance the mobile deployment of the detection framework.

Traditional image-sentence cross-modal retrieval methods usually aim to learn consistent representations of heterogeneous modalities, thereby to search similar instances in one modality according to the query from another modality in result. The basic assumption behind these methods is that parallel multi-modal data (i.e., different modalities of the same example are aligned) can be obtained in prior. In other words, the image-sentence cross-modal retrieval task is a supervised task with the alignments as ground-truths. However, in many real-world applications, it is difficult to realign a large amount of parallel data for new scenarios due to the substantial labor costs, leading the non-parallel multi-modal data and existing methods cannot be used directly. On the other hand, there actually exists auxiliary parallel multi-modal data with similar semantics, which can assist the non-parallel data to learn the consistent representations. Therefore, in this paper, we aim at “Alignment Efficient Image-Sentence Retrieval” (AEIR), which recurs to the auxiliary parallel image-sentence data as the source domain data, and takes the non-parallel data as the target domain data. Unlike single-modal transfer learning, AEIR learns consistent image-sentence cross-modal representations of target domain by transferring the alignments of existing parallel data. Specifically, AEIR learns the image-sentence consistent representations in source domain with parallel data, while transferring the alignment knowledge across domains by jointly optimizing a novel designed cross-domain cross-modal metric learning based constraint with intra-modal domain adversarial loss. Consequently, we can effectively learn the consistent representations for target domain considering both the structure and semantic transfer. Furthermore, extensive experiments on different transfer scenarios validate that AEIR can achieve better retrieval results comparing with the baselines.

Federated learning is a promising learning paradigm that allows collaborative training of models across multiple data owners without sharing their raw datasets. To enhance privacy in federated learning, multi-party computation can be leveraged for secure communication and computation during model training. This survey provides a comprehensive review on how to integrate mainstream multi-party computation techniques into diverse federated learning setups for guaranteed privacy, as well as the corresponding optimization techniques to improve model accuracy and training efficiency. We also pinpoint future directions to deploy federated learning to a wider range of applications.

In this study, we have developed a neural network aimed at enhancing the precision of neural distinguishers, demonstrating its capability to surpass DDT-based distinguishers in certain rounds. To extend the scope of our key recovery attack to additional rounds, we have diligently focused on improving both classical differentials and neural distinguishers. Consequently, we have successfully executed practical key recovery attacks on SIMECK32/64, effectively advancing the practical attack threshold by two additional rounds, allowing us to reach up to 17 rounds.

In this paper, we propose the community search framework searching polarized communities via adaptively fusing structure and attribute in attributed signed networks, which searches for two polarized subgraphs on an attributed signed network for given query nodes. We first conduct a analysis by the similarity of attributes between nodes. And we adaptively integrate topology and node attributes into an augmented signed network. Then, a spectral method based on generalized Rayleigh quotient is proposed. Finally, a linear programming problem is designed to detect polarized communities by local eigenspace. Experiments on real-world datasets demonstrate the effectiveness of our method.