Generative vision-language models like Stable Diffusion demonstrate remarkable capabilities in creative media synthesis, but they also pose substantial risks of producing unsafe, offensive, or culturally inappropriate content when prompted adversarially. Current defenses struggle to align outputs with human values without sacrificing generation quality or incurring high costs. To address these challenges, we introduce VALOR (Value-Aligned LLM-Overseen Rewriter), a modular, zero-shot agentic framework for safer and more helpful text-to-image generation. VALOR integrates layered prompt analysis with human-aligned value reasoning: a multi-level NSFW detector filters lexical and semantic risks; a cultural value alignment module identifies violations of social norms, legality, and representational ethics; and an intention disambiguator detects subtle or indirect unsafe implications. When unsafe content is detected, prompts are selectively rewritten by a large language model under dynamic, role-specific instructions designed to preserve user intent while enforcing alignment. If the generated image still fails a safety check, VALOR optionally performs a stylistic regeneration to steer the output toward a safer visual domain without altering core semantics. Experiments across adversarial, ambiguous, and value-sensitive prompts show that VALOR significantly reduces unsafe outputs by up to 100.00% while preserving prompt usefulness and creativity. These results highlight VALOR as a scalable and effective approach for deploying safe, aligned, and helpful image generation systems in open-world settings.

The rapid advancement of deep learning has turned models into highly valuable assets due to their reliance on massive data and costly training processes. However, these models are increasingly vulnerable to leakage and theft, highlighting the critical need for robust intellectual property protection. Model watermarking has emerged as an effective solution, with black-box watermarking gaining significant attention for its practicality and flexibility. Nonetheless, existing black-box methods often fail to better balance covertness (hiding the watermark to prevent detection and forgery) and robustness (ensuring the watermark resists removal)—two essential properties for real-world copyright verification. In this paper, we propose ComMark, a novel black-box model watermarking framework that leverages frequency-domain transformations to generate compressed, covert, and attack-resistant watermark samples by filtering out high-frequency information. To further enhance watermark robustness, our method incorporates simulated attack scenarios and a similarity loss during training. Comprehensive evaluations across diverse datasets and architectures demonstrate that ComMark achieves state-of-the-art performance in both covertness and robustness.

The growing presence of mobile and IoT devices has led to massive decentralized and evolving data, driving the rise of Federated Learning (FL) to enable collaborative training without data sharing. However, traditional FL assumes static data distributions, which is unrealistic for dynamic real-world environments. To address this challenge, Federated Class-Incremental Learning (FCIL) has emerged as a promising framework that enables flexible adaptation to newly introduced classes over time. Existing FCIL methods typically integrate old knowledge preservation into local client training. However, these methods cannot avoid spatial-temporal client drift caused by data heterogeneity and often incur significant computational and communication overhead, limiting practical deployment. To address these challenges simultaneously, we propose a novel approach, Spatial-Temporal Statistics Aggregation (STSA), which provides a unified framework to aggregate feature statistics both spatially (across clients) and temporally (across stages). The aggregated feature statistics are unaffected by data heterogeneity and can be used to update the classifier in closed form at each stage. Additionally, we introduce STSA-E, a communication-efficient variant that enables the server to approximate global second-order feature statistics using first-order statistics uploaded from clients. Theoretical analysis shows that it achieves similar performance to STSA with much lower communication overhead. Extensive experiments on three widely used FCIL datasets, with varying degrees of data heterogeneity, show that our method outperforms state-of-the-art FCIL methods in terms of performance, flexibility, and both communication and computation efficiency. The code is available at https://github.com/Yuqin-G/STSA.

Graph-structured data is foundational to numerous web applications, and watermarking is crucial for protecting their intellectual property and ensuring data provenance. Existing watermarking methods primarily operate on graph structures or entangled graph representations, which compromise the transparency and robustness of watermarks due to the information coupling in representing graphs and uncontrollable discretization in transforming continuous numerical representations into graph structures. This motivates us to propose DRGW, the first graph watermarking framework that addresses these issues through disentangled representation learning. Specifically, we design an adversarially trained encoder that learns an invariant structural representation against diverse perturbations and derives a statistically independent watermark carrier, ensuring both robustness and transparency of watermarks. Meanwhile, we devise a graph-aware invertible neural network to provide a lossless channel for watermark embedding and extraction, guaranteeing high detectability and transparency of watermarks. Additionally, we develop a structure-aware editor that resolves the issue of latent modifications into discrete graph edits, ensuring robustness against structural perturbations. Experiments on diverse benchmark datasets demonstrate the superior effectiveness of DRGW.

This paper investigates how to leverage emerging non-volatile memory (NVM) to enhance the performance of Log-Structure Merge (LSM) tree based key-value (KV) stores. We propose KVFG-DB, which efficiently integrates fine-block granularity and single-NVM-level compaction, to deliver high write and read performance with minimal write amplification and reduced write stalls. KVFG-DB leverages a streamlined SSTable layout called CSSTable to manage data blocks (each containing ordered KV pairs) and their indexes (i.e., the minimal and maximal keys of every block), optimizing both storage costs and compaction performance. It organizes the LSM-tree data as CSSTables into a single level on NVM, with the left area serving as a buffer to receive flushed data with substantially greater capacity, and the right area storing compacted data in a global order among CSSTables. A fine-grained compaction is then performed under various conditions to select the most relevant data blocks with intersecting keys from CSSTables in both areas, facilitating byte-addressable, fast parallel execution across multiple threads. As a result, KVFG-DB adaptively compacts data and quickly moves them from the left area to the right area to enhance performance efficiency, significantly reducing write amplification and further mitigating write stalls. Our extensive experimental studies demonstrate that KVFG-DB achieves 1.2× and 2× lower write amplification, compared with state-of-the-art KV stores MioDB and SLM-DB. Accordingly, KVFG-DB shows a 1.1× and 7.1× improvement in random write performance compared to them, with tail latency reduced by 1.1× and 7×, respectively.

As image data outsourcing to mobile cloud grows, data privacy has become a major concern. Privacy-preserving image retrieval aims to search over encrypted data without requiring decryption. However, existing schemes face three critical challenges: struggle to balance accuracy, efficiency, and security; limited scalability for large-scale image retrieval in multi-user settings; and vulnerability to security threats arising from user permission changes or key compromises. In this paper, we propose ELSEIR, a novel framework for accurate, efficient, and privacy-preserving image retrieval. ELSEIR leverages a deep hashing model to extract image feature vectors and designs an irreversible random hash code generation module that combines secure permutation keys with two differential privacy methods for privacy protection. To enable accurate searches in multi-user settings, ELSEIR introduces a key conversion protocol that allows the cloud to unify ciphertexts encrypted under different user keys via corresponding switch keys. Furthermore, we extend the framework to ABE-ELSEIR, which supports immediate and efficient user revocation. We further provide formal security proofs demonstrating that the proposed frameworks are resilient against known-plaintext and key collusion attacks. Extensive experiments on real-world datasets show that our scheme achieves accuracy comparable to the unprotected baseline, while surpassing existing approaches in both retrieval accuracy and efficiency.

Traditional Federated Learning (FL) necessitates numerous rounds of communication between the server and clients, posing significant challenges including high communication costs, connection drop risks and susceptibility to privacy attacks. One-shot FL has become a compelling learning paradigm to overcome above drawbacks by enabling the training of a global server model via a single communication round. However, existing one-shot FL methods suffer from expensive computation cost on the server or clients and cannot deal with non-IID (Independent and Identically Distributed) data stably and effectively. To address these challenges, this paper proposes FedCGS, a novel Federated learning algorithm that Capture Global feature Statistics leveraging pre-trained models. With global feature statistics, we achieve training-free and heterogeneity-resistant one-shot FL. Furthermore, we expand its application to personalization scenario, where clients only need execute one extra communication round with server to download global statistics. Extensive experimental results demonstrate the effectiveness of our methods across diverse data-heterogeneity settings.

For general users, training a neural network from scratch is usually challenging and labor-intensive. Fortunately, neural network zoos enable them to find a well-performing model for directly use or fine-tuning it in their local environments. Although current model retrieval solutions attempt to convert neural network models into vectors to avoid complex multiple inference processes required for model selection, it is still difficult to choose a suitable model due to inaccurate vectorization and biased correlation alignment between the query dataset and models. From the perspective of knowledge consistency, i.e., whether the knowledge possessed by the model can meet the needs of query tasks, we propose a model retrieval scheme, named Know2Vec, that acts as a black-box retrieval proxy for model zoo. Know2Vec first accesses to models via a black-box interface in advance, capturing vital decision knowledge from models while ensuring their privacy. Next, it employs an effective encoding technique to transform the knowledge into precise model vectors. Secondly, it maps the user's query task to a knowledge vector by probing the semantic relationships within query samples. Furthermore, the proxy ensures the knowledge-consistency between query vector and model vectors within their alignment space, which is optimized through the supervised learning with diverse loss functions, and finally it can identify the most suitable model for a given task during the inference stage. Extensive experiments show that our Know2Vec achieves superior retrieval accuracy against the state-of-the-art methods in diverse neural network retrieval tasks.

Federated Learning (FL) addresses data privacy and security in distributed machine learning. However, it performs poorly in real-world applications due to non-IID client data. Fortunately, recent advancements in data distillation offer a solution to generate a compressed and privacy-preserving distilled dataset that approximates the original dataset. Nonetheless, the current data distillation for FL usually indiscriminately distills information from all the samples through multiple communication rounds, which will inevitably generate redundant information and reduce the convergence speed. In this paper, we propose a novel local active data distillation method, which enables the global model to actively distill data that are beneficial for its training from each local dataset. Subsequently, we design a selective knowledge distillation to refine the global model by aligning it with an accurate expert model. Experimental results on multiple datasets have demonstrated the effectiveness of our method compared with the state-of-the-art methods.

Federated learning has been recently threatened by gradient inversion attacks, which reconstruct clients’ data from shared gradients. Among them, label recovery is proposed to restore training label distribution with the relationship between training labels and classifier gradients. However, these label recovery attacks are inapplicable for algorithms where classifier gradients are not shared. In this paper, we propose Feature-based Label Recovery (FLR) to restore label distribution without classifier gradients. Specifically, the honest-but-curious server simulates training process on an auxiliary dataset using various label distributions to collect feature anchors of a proxy dataset. It learns the relationship between the feature anchor and training label distribution by training a meta-model, which is then utilized to predict client’s label distribution. Importantly, FLR performs well even when the auxiliary and proxy dataset are random noise. Extensive experiments show that FLR can accurately restore the label distribution.

Structure-enhanced models get a leading performance on the link prediction task as they utilize selected structure features and Graph Neural Network (GNN) based node embeddings simultaneously. However, we observe that when graphs get sparser, these methods perform worse than classical GNN-based methods, which has a severe impact on their practical use. We prove that when the graph gets sparser, the distance between structure features gets smaller. We induce this is the underlying reason for hindering the model from giving a reasonable prediction and leading to performance degeneration. To overcome this problem, we first claim that models need to learn the importance of node embeddings based on the distance between structure features. However, there is a lack of research to efficiently estimate the distance information, and existing models fail to assign the importance properly. Then we design a method called DIP, which satisfies the relation requirement with a weighted term of node embeddings, and use node degree to estimate the distance information to get the weight. Experimental results show that DIP can significantly improve the accuracy of structure-enhanced link prediction models and solve the performance degeneration problem effectively. The code of DIP is publicly available at https://github.com/lzwqbh/DIP.

The remarkable image understanding and generation capabilities of diffusion models have made image editing a highly promising area of research. As a significant subtask within the field, object removal aims to remove objects from a specified region and fill the missing pixels with visually coherent and semantically sound content. Despite the great progress made in deep generative models, research in this area still faces several challenges: i. high expense of model training induced by the data scarcity and the difficulity in large-scale real data collection ii. current training-free methods are unable to drastically change the behavior of the attention layer that has been set during the pre-training phase for text-guided object inpainting. In this paper, we introduce HybridRemover, a two-stage diffusion scheme. We decouple the task into two subtasks: one to remove the specified objects from the target region and one to perform image restoration of the target region. By fine-tuning the SD-Inpainting model with a very small amount of data, we transform it into a model that focuses only on the complete removal of the object without considering the surrounding effects, and the overall repair of the image is taken care of by the SD-Inpainting model cascaded behind it. As a result of our efforts, our method achieved state-of-the-art performance in object removal tasks. Even when a strong perspective distortion gets involved, our method delivers exceptional results.

Efficiently managing data transmission across unreliable communication channels for diverse sources has attracted much attention due to its great practicability in the Internet of Things systems. Despite its significance in real-time wireless systems, ensuring stringent freshness requirements for each information source remains an understudied challenge, especially in scenarios demanding heterogeneous quality-of-service guarantees. In this paper, we study a scheduling problem of maximizing the accumulated information subject to different Age of Information (AoI) requirements for heterogeneous information sources. To that end, we first present a global optimal transmission scheduling policy when the source statistics are known a priori. Then, as this information is unknown, we formulate the problem as an AoI-constrained Multi-Armed bandit and propose an efficient learning algorithm, named O-SRS. Furthermore, we theoretically show that O-SRS achieves$O(\sqrt{T \log T})$regret while meeting the AoI requirement of each source. Finally, comprehensive simulations are conducted to demonstrate our theoretical results.

Federated Prompt Learning (FPL) efficiently alleviates data heterogeneity and reduces communication costs by introducing pre-trained models and prompt tuning. However, local prompts tend to favor diverse features and ignore public features captured under extreme data heterogeneity, which compromises the generalization ability of the global prompt by only aggregating local prompts. To address this challenge, we present Federated Prompt Learning with Gradient Rectification (FedGR), which modifies the gradient directions of local and global prompts to enhance the generalization of the global prompt. Specifically, we first introduce a zero-shot prompt as public knowledge and constrain the gradient of local prompts to consistently deviate from the public feature space to capture diverse features adequately. Then, we compute the angular bisector of local and zero-shot prompt gradients and replace the gradient of the global prompt with the gradient of the angular bisector to capture both diverse features and public features. Finally, the server-side global prompt can enhance generalization by aggregating all client-side global prompts. Extensive experiments with various types of heterogeneities have demonstrated that our FedGR outperforms the state-of-the-art methods.

Unsupervised cross-domain image retrieval (UCDIR) aims to retrieve images across different domains without the guidance of labels. However, existing UCDIR methods assume that data can be shared across domains in plaintext, which is often impractical due to strict data privacy protection policies. In this paper, we propose ShieldIR, a novel privacy-preserving unsupervised cross-domain image retrieval framework that enhances the retrieval performance across two domains while safeguarding data privacy. ShieldIR unifies intra-domain and cross-domain representation learning through a Dual Protection Transformation (DPT) module, which introduces a structured feature space via orthogonal projection and ensures data privacy by adding calibrated differential privacy noise. This transformation allows ShieldIR to preserve semantic structure while formally protecting private data. For intra-domain representation learning, ShieldIR enhances discriminability by using DPT to map prototypes into an independent feature space and subsequently aligning the resulting dual-protected prototypes with instance-level features. For cross-domain alignment, ShieldIR maps intra-domain features and cross-domain prototypes into a shared structured space using DPT, achieving semantic alignment under privacy constraints. Extensive experiments on real-world datasets demonstrate that our ShieldIR outperforms state-of-the-art methods while effectively protecting data privacy.

Test-time adaptation (TTA) for Vision-Language Models (VLMs) aims to enhance performance on unseen test data. However, existing methods struggle to achieve robust and continuous knowledge accumulation during test time. To address this, we propose Statistics Caching test-time Adaptation (SCA), a novel cache-based approach. Unlike traditional feature-caching methods prone to forgetting, SCA continuously accumulates task-specific knowledge from all encountered test samples. By formulating the reuse of past features as a least squares problem, SCA avoids storing raw features and instead maintains compact, incrementally updated feature statistics. This design enables efficient online adaptation without the limitations of fixed-size caches, ensuring that the accumulated knowledge grows persistently over time. Furthermore, we introduce adaptive strategies that leverage the VLM's prediction uncertainty to reduce the impact of noisy pseudo-labels and dynamically balance multiple prediction sources, leading to more robust and reliable performance. Extensive experiments demonstrate that SCA achieves compelling performance while maintaining competitive computational efficiency.

Searchable Symmetric Encryption (SSE) enables clients to make confidential queries over encrypted data while revealing some formally-defined leakage profiles. Despite the promising performance and application prospects of SSE, the recent leakage-abuse attacks show that a passive adversary can recover queries by exploiting patterns about data disclosed from leakage profiles. Among those attacks, the size pattern is a frequently exploited leakage. Although several countermeasures have been proposed, they can provide neither sufficient protection to mitigate size pattern leakage, nor sufficient scalability for large-scale databases. To address those challenges, we present an SGX-based size-pattern mitigation SSE schemeSMSSEwith two tailored response padding approaches and an I/O efficient disk-based index construction. In addition, we evaluate the size pattern leakage after padding through conditional entropy and differential privacy. Furthermore, we demonstrate the scalability robustness ofSMSSEon different databases by theoretically deducing the approximate boundary of index reading efficiency under a reasonable query distribution. Experiment results on representative real-world datasets show thatSMSSEcan provide high utility and strong protection against newly size pattern-based leakage-abuse attacks.

Traditional in-memory graph systems often suffer from scalability due to the limited capacity and volatility of DRAM. Emerging non-volatile memory (NVM) provides an opportunity to achieve highly scalable and high-performance graph stores for its large capacity and persistence characteristics. However, directly deploying current in-memory graph storage systems on NVM would cause significant inefficiencies in NVM access, as their graph organization designed for DRAM may incur higher write amplification, crash inconsistency and costly concurrency control overhead in NVM for write-intensive work-loads. In this paper, we propose FTGraph, a Flexible Tree-based Graph storage system, for both efficient dynamical graph updates and analysis. To achieve this goal, we introduce a novel degree-aware suffix bit tree to effectively manage vertices and edges of the graph, enabling adaptability to real-world power-law degree distributions while significantly reducing NVM writes. Based on it, we adopt two optimization methods, logical vertex ID translation and sequential storage, for vertices with very high degrees within the tree to enhance graph analysis operations. We further integrate 8B NVM atomic writes with optimistic version-based concurrency control through a dual bitmap design to ensure low-overhead, log-free crash consistency and reduce read-writer contention. Experimental results show that FTGraph achieves up to$\mathbf{21.2}\times$higher update performance and up to$\mathbf{85.4}\times$higher analysis performance, compared with state-of-the-art dynamic graph systems implemented on NVMs.

Few-shot relation extraction (FSRE) can alleviate the data scarcity problem in relation extraction. However, FSRE models often suffer a significant decline in performance when adapting to new domains. To overcome this issue, many researchers have focused on domain adaption FSRE (DAFSRE). Nevertheless, existing approaches primarily concentrate on the source domain, which makes it difficult to accurately transfer useful knowledge to the target domain. Additionally, the lack of distinction between relations further restricts the model performance. In this paper, we propose the domain-aware and co-adaptive feature transformation approach to address these issues. Specifically, we introduce a domain-aware transformation module that leverages the target domain distribution features to guide the domain-aware feature transformations. This can enhance the model’s adaptability across domains, leading to improved target domain performance. Furthermore, we design co-adaptive prototypical networks to perform co-adaptive feature transformation through a transformer mechanism. This results in more robust and distinguishable relation prototypes. Experiments on DAFSRE benchmark datasets demonstrate the effectiveness of our method, which outperforms existing models and achieves state-of-the-art performance.

The design of effective online caching policies is an increasingly important problem for content distribution networks, online recommender systems, and edge computing services, etc. Exiting literature usually tackles this problem through the lens of optimistic online learning and aims to achieve sublinear regret. In this paper, we focus on a non-trivial extension of classic online caching problem inspired by operational requirements of real-world systems including switching costs and long-term constraints. To tackle the challenges of switching costs and operational long-term constraints in the online caching, we introduce the Block-structured Follow-the-Regularized-Leader (B-FTRL) caching policy. Our approach incorporates a block structure that divides time into blocks to minimize caching switching costs. The theoretical analysis shows that B-FTRL achieves a utility regret bound of $O\left( {{T^{\frac{{2a - b + 1}}{{1 + a}}}} + {T^{\frac{b}{{1 + a}}}}} \right)$ and switching costs bound of $O\left( {{T^{\frac{1}{{1 + a}}}}} \right)$, where a and b are tunable algorithm parameters. By carefully selecting the values of a and b, we are able to limit the total regret to O(T2/3) while satisfying the operational long-term constraints in expectation. Additionally, we provide high-probability constraint violation bounds of $O\left( {\sqrt T } \right)$. The performance of the proposed algorithm is evaluated with detailed trace-driven numerical tests.

The Application Program Interface (API) security is crucial for data security as it ensures the safety and authority of data exchange between different applications. However, the absence of high-quality datasets significantly impedes the development of API anomaly detection. This paper presents a benchmark dataset and baselines for realistic API anomaly detection involving few-shot and unknown-risk scenarios. The dataset is synthesized using an Iterative data Generation approach with Dual-channel Filtering (IGDF). By leveraging large language models and dual-channel filtering models, we can iteratively generate and filter data, yielding a high-quality dataset. Moreover, we have developed baselines and conducted extensive experiments on the proposed dataset. The results indicate that few-shot and unknown-risk API anomaly detection remains a challenging task and still requires further research. All details and resources are released at https://github.com/yijunL/FUR-API.

federated learning has been introduced into person re-identification (Re-ID) to avoid personal image leakage in traditional centralized training. To address the key issue of statistic heterogeneity in different clients, several optimization methods have been proposed to alleviate the bias of the local models. However, besides statistic heterogeneity, feature heterogeneity (e.g., various angles, different illuminations) in different clients is more challenging in federated Re-ID. In this paper, we propose a meta-knowledge enhanced data augmentation method, where the global cross semantic feature transformations are provided to each client to perform local infinite augmentation to reduce the feature difference in different clients. Specifically, to capture the cross semantic feature transformations in each client, we calculate the covariance matrix of features with the local dataset as the transferable meta-knowledge. Then, this local meta-knowledge is propagated to the server for global aggregation. Subsequently, the aggregated meta-knowledge is sent back to each client for infinite data augmentation. Moreover, since the covariance matrix indicates variations in a client, we design a variation-balanced aggregation to replace the traditional data-size-balanced aggregation. To imitate the more challenging scenario of feature heterogeneity, we focus on the federated-by-camera setting to conduct experiments, where images collected in a camera are regarded as the dataset of a client. Extensive experimental results show that our method outperforms other state-of-the-art methods. Code is available at https://github.com/songchunli1999/MEDA.

Federated Learning (FL) has emerged as a promising approach to preserve privacy for distributed machine learning, through aggregating gradients calculated from local data of multiple clients. Recent studies show that gradients can be inverted to reconstruct private data retained by local clients in a process called gradient inversion. However, the performance of existing gradient inversion attacks declines as batch size increases. In this work, we propose a simple but effective method to tackle this challenge. Our method utilizes an interesting property between weights, embeddings and their corresponding gradients in fully connected layer, which enables the rapid and accurate recovery of embeddings. And these embeddings benefit the recovery of private data as additional prior knowledge. Extensive experiments demonstrate that our method can perform high-quality recovery of private data.

A critical challenge in federated learning is data heterogeneity, compounded by varying local data distribution, thereby significantly impacting the performance of both local and global models. Prior works struggle to effectively address data heterogeneity but ignore the feature skew. In this paper, we propose a novel method, Federated Learning with Semantic Enhancement (FedSE) to address this challenge. Specifically, we introduce semantic regularization terms and adaptive local aggregation to mitigate the drawback of local knowledge insufficiency and enhance the semantics of representations. Then, based on the neural collapse theory, we initialize a simplex equiangular tight frame structure (ETF) of the classifier and maintain its stability during local training, to achieve alignment in the feature space across different clients. Extensive experiments demonstrate our method achieves SOTA performance on several standard benchmark datasets, effectively alleviating the feature skew.

Person re-identification aims to retrieve the target person from a large-scale dataset, and its performance highly relies on a large, well-annotated training dataset. However, label noise generated by manual mislabeling is usually inevitable in real-world scenarios. An intuitive way to alleviate the effect of label noise is to identify and discard noisy data. Recently, most works recognize noisy samples with their global features, which will omit some noisy samples when they are subtly different from the clean ones. In this paper, we propose to recognize noisy data with local features based on uncertainty estimation. Specifically, for each input image, we randomly erase a part of its features to generate a local feature and propagate it to the softmax layer to obtain a local soft label. Then, we repeat the above operations to obtain multiple local soft labels. Subsequently, we estimate the uncertainty with these multiple local soft labels and utilize them to identify whether this sample is noisy. Finally, we design a noise-aware cross-entropy loss based on the estimated uncertainty to reduce the effect of noisy samples automatically. Experimental results on Market-1501 and CUHK03 datasets show the effectiveness of our proposed model, where at least 4.8% and 1.5% improvements are achieved compared with other methods under 0% and 10% noise settings on Market-1501 datasets. Code is available at https://github.com/songchunli1999/NACE.

Privacy-preserving content-based image retrieval aims to safeguard the security of outsourced private images while maintaining their searchability. However, existing schemes encounter challenges in striking a balance between security, accuracy, and efficiency, as well as difficulties in scaling to large-scale image retrieval in multi-user settings. In this paper, we propose a novel Efficient Large-Scale Encrypted Image Retrieval (ELSEIR) framework for outsourced data sharing. We first utilize a deep hashing model for image feature extraction. Building upon this, we design an irreversible random hash code generation method that incorporates permutation keys for personalized access and integrates differential privacy to further enhance data security. In our multi-user implementation, we distribute the switch keys to the cloud to standardize each key, enabling the accurate search. In addition, we have theoretically proven that our ELSEIR guarantees both outsourced data security and query user privacy. Extensive experiments on real-world datasets demonstrate that our ELSEIR yields comparable accuracy to the unprotected baseline while outperforming existing methods in terms of both retrieval accuracy and efficiency.

Dataset distillation, also known as dataset condensation, offers a possibility for compressing a large-scale dataset into a small-scale one (i.e., distilled dataset) while achieving similar performance during model training. This method effectively tackles the challenges of training efficiency and storage cost posed by the large-scale dataset. Existing dataset distillation methods can be categorized into Optimization-Oriented (OO)-based and Distribution-Matching (DM)-based methods. Since OO-based methods require bi-level optimization to alternately optimize the model and the distilled data, they face challenges due to high computational overhead in practical applications. Thus, DM-based methods have emerged as an alternative by aligning the prototypes of the distilled data to those of the original data. Although efficient, these methods overlook the diversity of the distilled data, which will limit the performance of evaluation tasks. In this paper, we propose a novel Diversified Semantic Distribution Matching (DSDM) approach for dataset distillation. To accurately capture semantic features, we first pre-train models for dataset distillation. Subsequently, we estimate the distribution of each category by calculating its prototype and covariance matrix, where the covariance matrix indicates the direction of semantic feature transformations for each category. Then, in addition to the prototypes, the covariance matrices are also matched to obtain more diversity for the distilled data. However, since the distilled data are optimized by multiple pre-trained models, the training process will fluctuate severely. Therefore, we match the distilled data of the current pre-trained model with the historical integrated prototypes. Experimental results demonstrate that our DSDM achieves state-of-the-art results on both image and speech datasets. Code is available at https://github.com/Li-Hongcheng/DSDM.

Multi-modal human action segmentation is a critical and challenging task with a wide range of applications. Nowadays, the majority of approaches concentrate on the fusion of dense signals (i.e., RGB, optical flow, and depth maps). However, the potential contributions of sparse IoT sensor signals, which can be crucial for achieving accurate recognition, have not been fully explored. To make up for this, we introduce a S parse s i gnal- g uided Transformer ( SigFormer ) to combine both dense and sparse signals. We employ mask attention to fuse localized features by constraining cross-attention within the regions where sparse signals are valid. However, since sparse signals are discrete, they lack sufficient information about the temporal action boundaries. Therefore, in SigFormer, we propose to emphasize the boundary information at two stages to alleviate this problem. In the first feature extraction stage, we introduce an intermediate bottleneck module to jointly learn both category and boundary features of each dense modality through the inner loss functions. After the fusion of dense modalities and sparse signals, we then devise a two-branch architecture that explicitly models the interrelationship between action category and temporal boundary. Experimental results demonstrate that SigFormer outperforms the state-of-the-art approaches on a multi-modal action segmentation dataset from real industrial environments, reaching an outstanding F1 score of 0.958. The codes and pre-trained models have been made available at https://github.com/LIUQI-creat/SigFormer .

We study the caching problem from an online learning point-of-view, i.e., no model assumptions and prior knowledge for the file request sequence. Our goal is to design an efficient online caching policy with minimal regret, i.e., minimizing the total number of cache miss with respect to the best static configuration in hindsight. Previous studies, such as Follow-The-Perturbed-Leader (FTPL) and Follow-The-Regularized-Leader (FTRL) caching policies, have provided some near-optimal results, but their theoretical performance guarantees only valid for the regime wherein all arrival requests could be seen by the cache, which is not the case in some practical scenarios. Hence our work closes this gap by considering the partial-observation regime wherein only requests for currently cached files are seen by the cache, which is more challenging and has not been studied before. We propose an online caching policy integrating the FTPL with a popularity estimation procedure called Geometric Resampling (GR), which is the first no-regret policy in this regime (achieve sublinear regret guarantee). Moreover, in the partial-observation regime, we also consider the caching problem with additional operational requirements of real-world systems, i.e., long-term constraints, and proposed a modified version of FTRL combining with GR to address this challenge setting. The theoretical analysis shows that this caching policy is able to achieve no-regret guarantee while satisfying the operational long-term constraints in expectation. Finally, we conduct numerical experiments to validate the theoretical guarantees of our proposed caching policies.

Signed graphs are prevalent data structures containing both positive and negative links. Recently, the fundamental network analysis task on signed graphs, namely link sign prediction, has received careful attention. Existing methods learn two target node representations independently, and the sign between these two nodes is predicted based on similarity. However, such a paradigm is node-centric that cannot distinguish node pairs with distinct contexts, thus lowering the prediction performance. Learning pair-centric representation is therefore a rewarding way to be aware of differences between pairs. There is no study yet on how to build such an appropriate representation that can effectively infer the sign between the target node pair. In this paper, we provide a new perspective to conduct link sign prediction within the paradigm of subgraph classification and propose a novel Subgraph-based link Sign Prediction (SSP) model. Technically, SSP uses importance-based sampling to extract an informative subgraph around each target node pair. For each subgraph, an innovative node labeling scheme is designed to encode its structural and signed information for representation learning. To further utilize the subgraph representation for imbalanced sign classification, SSP employs self-pruning contrastive learning to gain balanced representations. Extensive experiments on real-world datasets demonstrate that SSP consistently and significantly outperforms all the state-of-the-art baselines.

Few-shot relation extraction (FSRE) focuses on detecting new relations through a few annotated instances. Most existing works adopt prototypical network-based models for FSRE. They compute prototype representations for each class in the support set separately, and then use prototype representations for relation prediction. In this way, they learn only the knowledge of each class, regardless of the high-level interactions among these classes. However, these interactions can help the model understand diversity and improve discrimination, which is essential for FSRE, especially for similar relations’ prediction. In this work, we introduce a novel Biased Multi-granularity Interaction Prototype Network (BMIPN). Specifically, we mimic human cognitive processes to model explicit and adaptive interactions from intra- and inter-class aspects. Furthermore, we propose a novel biased contrastive learning method that encourages the model to focus on contrasting similar relations, generating discriminative and robust prototype representations. Experimental results on two benchmark datasets demonstrate that BMIPN outperforms state-of-the-art models and achieves better performance with respect to similar relations.

Recently, sequence generation methods are widely used in complex named entity recognition. By selecting high-related tokens to generate complex named entities, these methods obtain several achievements. However, due to lack of guidance in learning output format and ignoring labels in obtaining features, sequence generation methods suffer invalid output and inaccurate recognition. To solve that, we propose an Enhancing Entity Representation method with Prompt Guidance (ERPG). Specifically, in order to reduce invalid output, we design the candidate entity generation module that generate candidate entities and their labels as expected. Besides, to accurately recognize candidate entities, we propose candidate entity refine module, which obtain distinguishable candidate entity representations and filter them accurately. Based on that, our method finally outperforms baselines by 1.20, 1.62 and 0.69 F1 scores in ACE2004, GENIA and CADEC corpora, which proves the effectiveness in complex named entity recognition.

Relation extraction is extracting relational facts between target entities from plain text. It has attracted much interest and achieved improvement from research communities recently. However, effectively capturing relational information while ignoring intra-sentence noise from the text remains a challenge. To this end, we propose a novel RAN (Relation-aware Network) model for relation extraction. The basic idea of RAN is to model relation features, providing more accurate guidance for directly capturing relational information in a sentence, and improving the performance of relation extraction. Specifically, we first propose a multi-channel relation representation learning module. This module studies dependencies between the target entity pair in multiple sub-channels to model relation vectors. Then a dual attention mechanism-based relational words selection module is designed to highlight the relational information in the sentence of different dimensions, including semantic- and syntactic-level features. We evaluate our model on two public datasets: SemEval-2010 Task 8 and TACRED. Experiments on two benchmark datasets demonstrate the inspiring performance improvement over state-of-the-art models.

Cross-network node classification aims to leverage the abundant knowledge from a labeled source network to help classify the node in an unlabeled target network. However, existing methods assume that label sets are identical across domains, which is easily violated in practice. Hence, we attempt to integrate network embedding with universal domain adaptation, which transfers valuable knowledge across domains without assumption on the label sets, to assist in node classification. Nonetheless, the complex network relationships between nodes increase the difficulty of this universal domain adaptive node classification task. In this work, we propose a novel Universal Domain Adaptive Network Embedding (UDANE) framework, which learns transferable node representations across networks to succeed in such a task. Technically, we first adopt the cross-network node embedding component to model comprehensive node information of both networks. Then we employ the inter-domain adaptive alignment component to exploit and relate knowledge across domains, learning domain-invariant representation for knowledge transfer. In addition, the intra-domain contrastive alignment component is proposed to learn discriminative representations beneficial for classification by sufficiently utilizing unlabeled data in the target domain. Extensive experiments have been conducted on real-world datasets, demonstrating that the proposed UDANE model outperforms the state-of-the-art baselines by a large margin.

Multi-Codebook Quantization (MCQ) is a generalized version of existing codebook-based quantizations for Approximate Nearest Neighbor (ANN) search. Specifically, MCQ picks one codeword for each sub-codebook independently and takes the sum of picked codewords to approximate the original vector. The objective function involves no constraints, therefore, MCQ theoretically has the potential to achieve the best performance because solutions of other codebook-based quantization methods are all covered by MCQ's solution space under the same codebook size setting. However, finding the optimal solution to MCQ is proved to be NP-hard due to its encoding process, i.e., converting an input vector to a binary code. To tackle this, researchers apply constraints to it to find near-optimal solutions or employ heuristic algorithms that are still time-consuming for encoding. Different from previous approaches, this paper takes the first attempt to find a deep solution to MCQ. The encoding network is designed to be as simple as possible, so the very complex encoding problem becomes simply a feed-forward. Compared with other methods on three datasets, our method shows state-of-the-art performance. Notably, our method is 11× - 38× faster than heuristic algorithms for encoding, which makes it more practical for the real scenery of large-scale retrieval. Our code is publicly available: https://github.com/DeepMCQ/DeepQ.

Relation extraction is the task of extracting relational facts between entities from plain text. When the extraction scope is extended to the document level, entities may exist in dif-ferent sentences. This requires the model to consider the in-teraction between multiple sentences comprehensively. Thus, document-level relation extraction becomes especially chal-lenging. Most existing models adopt entity representation learning to tackle this challenge. Nevertheless, they gener-ally perform representations of individual entities rather than directly modeling the dependencies between entities reflect target relation, which may cause wrong relation reasoning. To solve this issue, we propose a novel dual reasoning-based pairwise representation network. Specifically, we perform the semantic and syntactic-based reasoning to model the associ-ation between entities, and towards the capture of relational information in the document through an unique contextual se-lection for each entity pair. Experiments on three benchmark datasets demonstrate the inspiring performance improvement over state-of-the-art relation extraction models.

Sequential recommendations can dynamically model user interests, which has great value since users' interests may change rapidly with time. Traditional sequential recommendation methods assume that the user behaviors are rigidly ordered and sequentially dependent. However, some user behaviors have flexible orders, meaning the behaviors may occur in any order and are not sequentially dependent. Therefore, traditional methods may capture inaccurate user interests based on wrong dependencies. Motivated by this, several methods identify flexible orders by continuity or similarity. However, these methods fail to comprehensively understand the nature of flexible orders since continuity or similarity do not determine order flexibilities. Therefore, these methods may misidentify flexible orders, leading to inappropriate recommendations. To address these issues, we propose a Flexible Order aware Sequential Recommendation (FOSR) method to identify flexible orders comprehensively. We argue that orders' flexibilities are highly related to the frequencies of item pair co-occurrences. In light of this, FOSR employs a probabilistic based flexible order evaluation module to simulate item pair frequencies and infer accurate order flexibilities. The frequency labeling module extracts labels from the real item pair frequencies to guide the order flexibility measurement. Given the measured order flexibilities, we develop a flexible order aware self-attention module to model dependencies from flexible orders comprehensively and learn dynamic user interests effectively. Extensive experiments on four benchmark datasets show that our model outperforms various state-of-the-art sequential recommendation methods.

Human motion transfer refers to synthesizing photo-realistic and temporally coherent videos that enable one person to imitate the motion of others. However, current synthetic videos suffer from the temporal inconsistency in sequential frames that significantly degrades the video quality, yet is far from solved by existing methods in the pixel domain. Recently, some works on DeepFake detection try to distinguish the natural and synthetic images in the frequency domain because of the frequency insufficiency of image synthesizing methods. Nonetheless, there is no work to study the temporal inconsistency of synthetic videos from the aspects of the frequency-domain gap between natural and synthetic videos. Therefore, in this paper, we propose to delve into the frequency space for temporally consistent human motion transfer. First of all, we make the first comprehensive analysis of natural and synthetic videos in the frequency domain to reveal the frequency gap in both the spatial dimension of individual frames and the temporal dimension of the video. To close the frequency gap between the natural and synthetic videos, we propose a novel Frequency-based human MOtion TRansfer framework, named FreMOTR, which can effectively mitigate the spatial artifacts and the temporal inconsistency of the synthesized videos. FreMOTR explores two novel frequency-based regularization modules: 1) the Frequency-domain Appearance Regularization (FAR) to improve the appearance of the person in individual frames and 2) Temporal Frequency Regularization (TFR) to guarantee the temporal consistency between adjacent frames. Finally, comprehensive experiments demonstrate that the FreMOTR not only yields superior performance in temporal consistency metrics but also improves the frame-level visual quality of synthetic videos. In particular, the temporal consistency metrics are improved by nearly 30% than the state-of-the-art model.

Human Video Motion Transfer (HVMT) aims to, given an image of a source person, generate his/her video that imitates the motion of the driving person. Existing methods for HVMT mainly exploit Generative Adversarial Networks (GANs) to perform the warping operation based on the flow estimated from the source person image and each driving video frame. However, these methods always generate obvious artifacts due to the dramatic differences in poses, scales, and shifts between the source person and the driving person. To overcome these challenges, this paper presents a novel REgion-to-whole human MOtion Transfer (REMOT) framework based on GANs. To generate realistic motions, the REMOT adopts a progressive generation paradigm: it first generates each body part in the driving pose without flow-based warping, then composites all parts into a complete person of the driving motion. Moreover, to preserve the natural global appearance, we design a Global Alignment Module to align the scale and position of the source person with those of the driving person based on their layouts. Furthermore, we propose a Texture Alignment Module to keep each part of the person aligned according to the similarity of the texture. Finally, through extensive quantitative and qualitative experiments, our REMOT achieves state-of-the-art results on two public benchmarks.

Existing studies for gait recognition are dominated by in-the-lab scenarios. Since people live in real-world senses, gait recognition in the wild is a more practical problem that has recently attracted the attention of the community of multimedia and computer vision. Current methods that obtain state-of-the-art performance on in-the-lab benchmarks achieve much worse accuracy on the recently proposed in-the-wild datasets because these methods can hardly model the varied temporal dynamics of gait sequences in unconstrained scenes. Therefore, this paper presents a novel multi-hop temporal switch method to achieve effective temporal modeling of gait patterns in real-world scenes. Concretely, we design a novel gait recognition network, named Multi-hop Temporal Switch Network (MTSGait), to learn spatial features and multi-scale temporal features simultaneously. Different from existing methods that use 3D convolutions for temporal modeling, our MTSGait models the temporal dynamics of gait sequences by 2D convolutions. By this means, it achieves high efficiency with fewer model parameters and reduces the difficulty in optimization compared with 3D convolution-based models. Based on the specific design of the 2D convolution kernels, our method can eliminate the misalignment of features among adjacent frames. In addition, a new sampling strategy, i.e., non-cyclic continuous sampling, is proposed to make the model learn more robust temporal features. Finally, the proposed method achieves superior performance on two public gait in-the-wild datasets, i.e., GREW and Gait3D, compared with state-of-the-art methods.

Community search, also known as local community detection, is designed to obtain the community structure containing the given query nodes. At present, community search technology has been widely studied, but most of them ignore the effect of node attributes. Some studies propose attributed community search methods and usually adopt a two-phase approach. Generally, the subgraph with structure cohesiveness is obtained first and then the subgraph with keyword cohesiveness is returned as the final result on this basis. In practice, approaches of this kind may lead to incomplete or incorrect results, because information from one single aspect cannot accurately describe the feature of nodes. That is, topology information and attribute information of the network should be complementary. In this paper, we propose an attributed community model called RCS(Representation-based Community Search). Firstly we apply random-walk based representation learning technology to combine topology information and attribute information of nodes. Secondly we propose a new community model to formulate the problem that what is a good community based on the representation information. Then a series of algorithms are designed to fetch a cohesive community whose vertices are tightly connected and closely related to query nodes. Finally the effectiveness and efficiency of RCS are verified by extensive experiments on several real-world networks with ground-truth cnmmunities.

Heterogeneous side information has been widely used in recommender systems to alleviate the data sparsity problem. However, the heterogeneous side information in existing methods provides insufficient guidance for predicting user preferences as its effect is inevitably weakened during utilization. Furthermore, most existing methods cannot effectively utilize the heterogeneous side information to understand users and items. They often neglect the interrelation among various types of heterogeneous side information of a user or an item. As a result, it is difficult for existing methods to comprehensively understand users and items so that the recommender system recommends inappropriate items to users. To overcome the above drawbacks, we propose an interrelation learning-based recommendation method with iterative heterogeneous side information guidance (ILIG). ILIG includes two modules: 1) Iterative Heterogeneous Side Information Guidance Module. It uses heterogeneous side information to iteratively guide the prediction of user preferences, which effectively enhances the effect of the heterogeneous side information. 2) Interrelation Learning-based Portrait Construction Module. It captures the interrelation among various types of heterogeneous side information to comprehensively learn the representations of users and items. To demonstrate the effectiveness of ILIG, we conduct extensive experiments on Movielens-100K, Movielens-1M, and BookCrossing datasets. The experimental results show that ILIG outperforms the state-of-the-art recommender systems.

The RGB-infrared cross-modality person re-identification (ReID) task aims to recognize the images of the same identity between the visible modality and the infrared modality. Existing methods mainly use a two-stream architecture to eliminate the discrepancy between the two modalities in the final common feature space, which ignore the single space of each modality in the shallow layers. To solve it, in this paper, we present a novel multi-feature space joint optimization (MSO) network, which can learn modality-sharable features in both the single-modality space and the common space. Firstly, based on the observation that edge information is modality-invariant, we propose an edge features enhancement module to enhance the modality-sharable features in each single-modality space. Specifically, we design a perceptual edge features (PEF) loss after the edge fusion strategy analysis. According to our knowledge, this is the first work that proposes explicit optimization in the single-modality feature space on cross-modality ReID task. Moreover, to increase the difference between cross-modality distance and class distance, we introduce a novel cross-modality contrastive-center (CMCC) loss into the modality-joint constraints in the common feature space. The PEF loss and CMCC loss jointly optimize the model in an end-to-end manner, which markedly improves the network's performance. Extensive experiments demonstrate that the proposed model significantly outperforms state-of-the-art methods on both the SYSU-MM01 and RegDB datasets.

Given a network and a set of seeds related to each other, the problem of relation discovery from focusing seeds aims to discover the relations among all the seeds. Due to its wide applications, the task has been well studied in the literature. However, when facing applications where maybe not all the seeds relate to each other, methods for the task will discover many vertices unrelated to the seeds. To support such applications, a new problem called adaptive relation discovery from focusing seeds (A-RDFS) is proposed and studied in this article. Given a network and a set of seeds that may not be related to each other, discover additional vertices to reveal the relations among the seeds which are related to each other. To solve the A-RDFS problem, a relation sensitive subgraph structure called the α- relation core is proposed to find vertices related to a subset of the seeds. Thereafter, a metric called the relation quality is proposed to measure the quality of discovered relations. The metric is positively correlated with the α value of each discovered α-relation core. Hence, by maximizing the relation quality, a set of α-relation cores with large α values can be discovered, which reveals the relations among the seeds related to each other. Two algorithms are developed to optimize the relation quality. Then, using the algorithms as subroutines, the OPT-A-RDFS method is designed for the A-RDFS problem. Extensive experimental results demonstrate the performance of our methods.

As a major kind of query-dependent community detection, community search finds a densely connected subgraph containing a set of query nodes. As density is the major consideration of community search, most methods of community search often find a dense subgraph with many vertices far from the query nodes, which are not very related to the query nodes. Motivated by this, a new problem called community focusing (CF) is studied. It finds a community where the members are close and densely connected to the query nodes. A distance-sensitive dense subgraph structure called β-attention-core is proposed to remove the vertices loosely connected to or far from the query nodes, and a combinational density is designed to guarantee the density of a subgraph. Then CF is formalized as finding a subgraph with the largest combinational density among the β-attention-core subgraphs containing the query nodes with the largest β. Thereafter, effective methods are devised for CF. Furthermore, a speed-up strategy is developed to make the methods scalable to large networks. Extensive experimental results on real and synthetic networks demonstrate the performance of our methods.

Cross-modal hashing has attracted considerable attention for large-scale multimodal retrieval task. A majority of hashing methods have been proposed for cross-modal retrieval. However, these methods inadequately focus on feature learning process and cannot fully preserve higher-ranking correlation of various item pairs as well as the multi-label semantics of each item, so that the quality of binary codes may be downgraded. To tackle these problems, in this paper, we propose a novel deep cross-modal hashing method, called Adversary Guided Asymmetric Hashing (AGAH). Specifically, it employs an adversarial learning guided multi-label attention module to enhance the feature learning part which can learn discriminative feature representations and keep the cross-modal invariability. Furthermore, in order to generate hash codes which can fully preserve the multi-label semantics of all items, we propose an asymmetric hashing method which utilizes a multi-label binary code map that can equip the hash codes with multi-label semantic information. In addition, to ensure higher-ranking correlation of all similar item pairs than those of dissimilar ones, we adopt a new triplet-margin constraint and a cosine quantization technique for Hamming space similarity preservation. Extensive empirical studies show that AGAH outperforms several state-of-the-art methods for cross-modal retrieval.

Facing up to the incessant growth of complex networks, more and more researchers start turning to a multilevel computing paradigm with high scalability for clustering. By virtue of iterative coarsening level by level, the clustering results which are obtained from the coarsest network and then projected to the original network, is superior to the ones from mining the original complex network explicitly. Empirical works reflect that the local-aggregation characteristic is a key point for multilevel clustering algorithms, thus techniques like modularity, label propagation etc. are used to discover the micro-clusters for coarsening. In this paper, we propose a scalable clustering algorithm via a triangle folding processing for complex networks(SCAFT). Based on the strong cluster property of triangle, we fold each traversed triangle of the network into a superverex to realize coarsening. And each generated coarsened network by iteration is capable of reserving the cluster structures of last level network, or even the intrinsic cluster structures of original complex network, improving the computational accuracy. What's more, a streaming algorithm is embedded in our novel approach to generate a serial input sequence of vertices, reducing the heavy burdens of memory usage of system. Experimental results on real-world complex networks show that, SCAFT outperforms the state-of-the-art multilevel clustering algorithms in terms of clustering accuracy, running time, especially in memory usage.

Hadoop is a popular open-source framework that allows distributed analysis of large datasets using the MapReduce programming model. A distributed file system HDFS is implemented to provide high-throughput access to datasets. HDFS can achieve high performance metadata service but has two disadvantages. First, when the metadata server stores metadata on persistent devices, it is restricted to read and write operations of local disks. Second, it also lacks effective methods for metadata synchronization and replication, which is critical for metadata availability and reliability. In this research, we introduce a novel Virtual Shared Storage Pool (VSSP) concept and design for storing and sharing metadata in HDFS. The VSSP is a virtual storage device which is built on existing servers and transparent to upper layers. Two strategies, a journal synchronization based on the 2PC protocol and a fine-grained image replication, are introduced in the VSSP according to different metadata access features. The VSSP not only reduces the overhead on metadata modification operations, but also improves the I/O performance for namespace storage. Experimental results show that the VSSP improved the average performance by 40.51% and 23.46% when writing logs compared with the BookKeeper and Hadoop QJM. The average image read and write throughput was nearly 5 times and 2.4 times better than NFS and the original approach. These results confirm that the proposed VSSP solution significantly improves the metadata access performance, scalability, and reliability for HDFS.

Nowadays Hadoop has become a leading architecture for large-scale data processing. One of the efficient ways to accelerate data processing is column-oriented storage technique which has been integrated into Hadoop family recently. However, how to design an appropriate attribute clustering algorithm to achieve optimal data processing performance for column-oriented hadoop system is still a big problem. In this paper, we propose a novel algorithm called CHAC to solve this problem. Both cases of overlapping attribute cluster and non-overlapping attribute cluster are considered in CHAC. In addition, an adjustable parameter is also taken into account to prohibit excessive attribute redundancy via limiting space overhead. The experimental results on TPC-H Benchmark demonstrate the efficiency and effectiveness of the proposed algorithm.