Graph edit distance (GED) is a fundamental graph similarity metric. GED computation is NP-hard [10], and exact GED computation is only feasible for small graphs. Therefore, many methods of approximate GED computation have been proposed in the literature. In this paper, we select the five representative GED approximation methods and compare their performance on two real-world datasets. We observe that non-heuristic algorithms such as LSa [1] are fast and accurate in computing true GED for small graphs, and heuristic algorithms such as GENN [4] are very effective in computing the estimated path cost. This effort helps us pinpoint suitable algorithms for different applications.

Rational secure multi-party computation means two or more rational parties complete a function on private inputs. Unfortunately, players sending false information can prevent the protocol from executing correctly, which will destroy the fairness of the protocol. To ensure the fairness of the protocol, the existing works on achieving fairness by specific utility functions. In this paper, we leverage game theory to propose the direction entropy-based solution. To this end, we utilize the direction entropy to examine the player's strategy uncertainty and quantify its strategy from different dimensions. Then, we provide mutual information to construct a new utility for the players. What's more, we measure the mutual information of players to appraise their strategies. By analyzing and proofing of protocol, we show that the protocol reaches a Nash equilibrium when players choose a cooperative strategy. Furthermore, we solve the fairness of the protocol. Compared to the previous approaches, our protocol is not required deposits and design-specific utility functions.

Object detection at the edge side is a common task in various environments. The deployment of convolutional neural networks in intelligent edge systems is very challenging because of the highly constrained main-memory space. This study aims at operating neural networks with a reduced memory requirement. The basic idea is that tasks of the same type would involve the same critical subnetwork. We propose identifying the critical network connections by considering the importance of channels. During runtime, the proposed method detects the task types and timely swaps the model parameters of the critical subnetworks from the external storage into dynamic random access memory (DRAM). Compared with conventional network pruning, the proposed approach further reduced the DRAM requirement by 34.6% while maintaining a high inference accuracy.

While gradient aggregation playing a vital role in federated or collaborative learning, recent studies have revealed that gradient aggregation may suffer from some attacks, such as gradient inversion, where the private training data can be recovered from the shared gradients. However, the performance of the existing attack methods is limited because they usually require prior knowledge in Batch Normalization and could only reconstruct a single image or a small batch one. To make the attacks less restrictive and more applicable, we propose an effective and practical gradient inversion method in this paper. Specifically, we use cosine similarity to measure the difference of gradients between the synthesized and ground-truth images, and then construct an input regularization for the fully connected layer to ensure the fidelity of the image. Moreover, we apply the total variation denoising strategy to the convolution feature map for further improving the smoothness of the reconstructed image. Experimental results demonstrate that our method can reconstruct high fidelity training data on a large batch size for complex data sets, such as ImageNet.

A contrastive distortion-level learning-based no-reference image-quality assessment (NR-IQA) framework is proposed in this study to further effectively model various distortion types with the same or different distortion levels. The proposed method aims to improve the prediction accuracy of NR-IQA. The proposed method consists of three parts: multiscale distortion-level representation learning, single-image NR-IQA, and a representation affinity module, which can reduce NR-IQA computational complexity while maintaining a low-distortion representation of high-distortion inputs. The proposed NR-IQA method aims to extract distributional features of samples in real distorted images and predict ambiguity based on distortion-level learning. Experimental results show that by comparing on many NR-IQA data sets the proposed method can outperform state-of-the-art methods.

One key factor able to boost the applications of artificial intelligence (AI) in security-sensitive domains is to leverage them responsibly, which is engaged in providing explanations for AI. To date, a plethora of explainable artificial intelligence (XAI) has been proposed to help users interpret model decisions. However, given its data-driven nature, the explanation itself is potentially susceptible to a high risk of exposing privacy. In this paper, we first show that the existing XAI is vulnerable to model extraction attacks and then present an XAI-aware dual-task model extraction attack (DTMEA). DTMEA can attack a target model with explanation services, that is, it can extract both the classification and explanation tasks of the target model. More specifically, the substitution model extracted by DTMEA is a multitask learning architecture, consisting of a sharing layer and two task-specific layers for classification and explanation. To reveal which explanation technologies are more vulnerable to expose privacy information, we conduct an empirical evaluation of four major explanation types in the benchmark data set. Experimental results show that the attack accuracy of DTMEA outperforms the predicted-only method with up to 1.25%, 1.53%, 9.25%, and 7.45% in MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100, respectively. By exposing the potential threats on explanation technologies, our research offers the insights to develop effective tools that are able to trade off security-sensitive relationships.

Three-dimensional (3D) charge-trap based solid-state-drivers (SSDs) have become an emerging storage solution in recent years. One-shot-programming in 3D charge-trap based SSDs could deliver a maximized system input/output (I/O) throughput at the cost of degraded Quality-of-Service (QoS) performance. This paper proposes reinforcement-learning based one-shot-programming (RLOSP), a reinforcement learning based approach to improve the QoS performance for 3D charge-trap based SSDs. By learning the I/O patterns of the workload environments as well as the device internal status, the proposed approach could properly choose requests in the device queue, and allocate physical addresses for these requests during one-shot-programming. In this manner, the storage device could deliver an improved QoS performance. Experimental results reveal that the proposed approach could reduce the worst-case latency at the 99.9th percentile by 37.5%–59.2%, with an optimal system I/O throughput.