Dear Editor,This letter presents a distributed adaptive second-order latent factor(DAS) model for addressing the issue of high-dimensional and incomplete data representation. Compared with first-order optimizers, a se...
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Dear Editor,This letter presents a distributed adaptive second-order latent factor(DAS) model for addressing the issue of high-dimensional and incomplete data representation. Compared with first-order optimizers, a second-order optimizer has stronger ability in approaching a better solution when dealing with the non-convex optimization problems, thus obtaining better performance in extracting the latent factors(LFs) well representing the known information from high-dimensional and incomplete data.
The virtual private cloud service currently lacks a real-time end-to-end consistency validation mechanism, which prevents tenants from receiving immediate feedback on their requests. Existing solutions consume excessi...
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The virtual private cloud service currently lacks a real-time end-to-end consistency validation mechanism, which prevents tenants from receiving immediate feedback on their requests. Existing solutions consume excessive communication and computational resources in such large-scale cloud environments, and suffer from poor timeliness. To address these issues, we propose a lightweight consistency validation mechanism that includes real-time incremental validation and periodic full-scale validation. The former leverages message layer aggregation to enable tenants to swiftly determine the success of their requests on hosts with minimal communication overhead. The latter utilizes lightweight validation checksums to compare the expected and actual states of hosts locally, while efficiently managing the checksums of various host entries using inverted indexing. This approach enables us to efficiently validate the complete local configurations within the limited memory of hosts. In summary, our proposed mechanism achieves closed-loop implementation for new requests and ensures their long-term effectiveness.
Recently, redactable blockchain has been proposed and leveraged in a wide range of real systems for its unique properties of decentralization, traceability, and transparency while ensuring controllable on-chain data r...
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Recently, redactable blockchain has been proposed and leveraged in a wide range of real systems for its unique properties of decentralization, traceability, and transparency while ensuring controllable on-chain data redaction. However, the development of redactable blockchain is now obstructed by three limitations, which are data privacy breaches, high communication overhead, and low searching efficiency, respectively. In this paper, we propose PriChain, the first efficient privacy-preserving fine-grained redactable blockchain in decentralized settings. PriChain provides data owners with rights to control who can read and redact on-chain data while maintaining downward compatibility, ensuring the one who can redact will be able to read. Specifically, inspired by the concept of multi-authority attribute-based encryption, we utilize the isomorphism of the access control tree, realizing fine-grained redaction mechanism, downward compatibility, and collusion resistance. With the newly designed structure, PriChain can realize O（n） communication and storage overhead compared to prior O（n2） schemes. Furthermore, we integrate multiple access trees into a tree-based dictionary, optimizing searching efficiency. Theoretical analysis proves that PriChain is secure against the chosen-plaintext attack and has competitive complexity. The experimental evaluations show that PriChain realizes 10× efficiency improvement of searching and 100× lower communication and storage overhead on average compared with existing schemes.
The distribution of the nuclear ground-state spin in a two-body random ensemble(TBRE)was studied using a general classification neural network(NN)model with two-body interaction matrix elements as input features and t...
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The distribution of the nuclear ground-state spin in a two-body random ensemble(TBRE)was studied using a general classification neural network(NN)model with two-body interaction matrix elements as input features and the corresponding ground-state spins as labels or output *** quantum many-body system problem exceeds the capability of our optimized NNs in terms of accurately predicting the ground-state spin of each sample within the ***,our NN model effectively captured the statistical properties of the ground-state spin because it learned the empirical regularity of the ground-state spin distribution in TBRE,as discovered by physicists.
Witness encryption(WE) is a novel type of cryptographic primitive that enables a message to be encrypted via an NP instance. Anyone who possesses a solution to this instance(i.e., a witness) can then recover the messa...
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Witness encryption(WE) is a novel type of cryptographic primitive that enables a message to be encrypted via an NP instance. Anyone who possesses a solution to this instance(i.e., a witness) can then recover the message from the *** introduce a variant of WE that allows ciphertext updates, referred to as ciphertext updateable WE(CUWE). With CUWE,a user can encrypt a message using an instance x and a tag t, and those who possess a valid witness w for x and match the access policy defined by tag t can decrypt the message. Furthermore, CUWE allows for the use of an update token to change the tag t of ciphertext to a different tag. This feature enables fine-grained access control, even after the ciphertext has been created, thereby significantly increasing the usefulness of the WE scheme. We demonstrate that such a WE framework with an updatable ciphertext scheme can be constructed using our puncturable instance-based deterministic encryption(PIDE) and indistinguishability obfuscation(iO). We also propose an instantiation of PIDE utilizing puncturable pseudorandom functions(PRFs) that provide(selectively) indistinguishable security. Finally, we expand our CUWE to ciphertext-updatable functional WE(CUFWE), which offers enhanced data access control.
Anomaly detection(AD) has been extensively studied and applied across various scenarios in recent years. However, gaps remain between the current performance and the desired recognition accuracy required for practical...
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Anomaly detection(AD) has been extensively studied and applied across various scenarios in recent years. However, gaps remain between the current performance and the desired recognition accuracy required for practical *** paper analyzes two fundamental failure cases in the baseline AD model and identifies key reasons that limit the recognition accuracy of existing approaches. Specifically, by Case-1, we found that the main reason detrimental to current AD methods is that the inputs to the recovery model contain a large number of detailed features to be recovered, which leads to the normal/abnormal area has not/has been recovered into its original state. By Case-2, we surprisingly found that the abnormal area that cannot be recognized in image-level representations can be easily recognized in the feature-level representation. Based on the above observations, we propose a novel recover-then-discriminate(ReDi) framework for *** takes a self-generated feature map(e.g., histogram of oriented gradients) and a selected prompted image as explicit input information to address the identified in Case-1. Additionally, a feature-level discriminative network is introduced to amplify abnormal differences between the recovered and input representations. Extensive experiments on two widely used yet challenging AD datasets demonstrate that ReDi achieves state-of-the-art recognition accuracy.
Many networks exhibit the core/periphery ***/periphery structure is a type of meso-scale structure that consists of densely connected core nodes and sparsely connected peripheral *** nodes tend to be well-connected,bo...
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Many networks exhibit the core/periphery ***/periphery structure is a type of meso-scale structure that consists of densely connected core nodes and sparsely connected peripheral *** nodes tend to be well-connected,both among themselves and to peripheral nodes,which tend not to be well-connected to other *** this brief report,we propose a new method to detect the core of a network by the centrality of each *** is discovered that such nodes with non-negative centralities often consist in the core of the *** simulation is carried out on different real *** results are checked by the objective *** checked results may show the effectiveness of the simulation results by the centralities of the nodes on the real ***,we discuss the characters of networks with the single core/periphery structure and point out the scope of the application of our method at the end of this paper.
Edge closeness and betweenness centralities are widely used path-based metrics for characterizing the importance of edges in *** general graphs,edge closeness centrality indicates the importance of edges by the shorte...
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Edge closeness and betweenness centralities are widely used path-based metrics for characterizing the importance of edges in *** general graphs,edge closeness centrality indicates the importance of edges by the shortest distances from the edge to all the other *** betweenness centrality ranks which edges are significant based on the fraction of all-pairs shortest paths that pass through the ***,extensive research efforts go into centrality computation over general graphs that omit time ***,numerous real-world networks are modeled as temporal graphs,where the nodes are related to each other at different time *** temporal property is important and should not be neglected because it guides the flow of information in the *** state of affairs motivates the paper’s study of edge centrality computation methods on temporal *** introduce the concepts of the label,and label dominance relation,and then propose multi-thread parallel labeling-based methods on OpenMP to efficiently compute edge closeness and betweenness centralities *** types of optimal temporal *** edge closeness centrality computation,a time segmentation strategy and two observations are presented to aggregate some related temporal edges for uniform *** edge betweenness centrality computation,to improve efficiency,temporal edge dependency formulas,a labeling-based forward-backward scanning strategy,and a compression-based optimization method are further proposed to iteratively accumulate centrality *** experiments using 13 real temporal graphs are conducted to provide detailed insights into the efficiency and effectiveness of the proposed *** with state-ofthe-art methods,labeling-based methods are capable of up to two orders of magnitude speedup.
Adversarial examples(AEs) are an additive amalgamation of clean examples and artificially malicious perturbations. Attackers often leverage random noise and multiple random restarts to initialize perturbation starting...
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Adversarial examples(AEs) are an additive amalgamation of clean examples and artificially malicious perturbations. Attackers often leverage random noise and multiple random restarts to initialize perturbation starting points, thereby increasing the diversity of AEs. Given the non-convex nature of the loss function, employing randomness to augment the attack's success rate may lead to considerable computational overhead. To overcome this challenge,we introduce the one-hot mean square error loss to guide the initialization. This loss is combined with the strongest first-order attack, the projected gradient descent, alongside a dynamic attack step size adjustment strategy to form a comprehensive attack process. Through experimental validation, we demonstrate that our method outperforms baseline attacks in constrained attack budget scenarios and regular experimental settings. This establishes it as a reliable measure for assessing the robustness of deep learning models. We explore the broader application of this initialization strategy in enhancing the defense impact of few-shot classification models. We aspire to provide valuable insights for the community in designing attack and defense mechanisms.
Video question answering(VideoQA) is a challenging yet important task that requires a joint understanding of low-level video content and high-level textual semantics. Despite the promising progress of existing efforts...
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Video question answering(VideoQA) is a challenging yet important task that requires a joint understanding of low-level video content and high-level textual semantics. Despite the promising progress of existing efforts, recent studies revealed that current VideoQA models mostly tend to over-rely on the superficial correlations rooted in the dataset bias while overlooking the key video content, thus leading to unreliable results. Effectively understanding and modeling the temporal and semantic characteristics of a given video for robust VideoQA is crucial but, to our knowledge, has not been well investigated. To fill the research gap, we propose a robust VideoQA framework that can effectively model the cross-modality fusion and enforce the model to focus on the temporal and global content of videos when making a QA decision instead of exploiting the shortcuts in datasets. Specifically, we design a self-supervised contrastive learning objective to contrast the positive and negative pairs of multimodal input, where the fused representation of the original multimodal input is enforced to be closer to that of the intervened input based on video perturbation. We expect the fused representation to focus more on the global context of videos rather than some static keyframes. Moreover, we introduce an effective temporal order regularization to enforce the inherent sequential structure of videos for video representation. We also design a Kullback-Leibler divergence-based perturbation invariance regularization of the predicted answer distribution to improve the robustness of the model against temporal content perturbation of videos. Our method is model-agnostic and can be easily compatible with various VideoQA backbones. Extensive experimental results and analyses on several public datasets show the advantage of our method over the state-of-the-art methods in terms of both accuracy and robustness.
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