Citation recommendation aims to provide researchers with the most relevant references for their manuscripts, helping them swiftly discover pertinent studies and bolster the reliability of their arguments. However, some individuals manipulate these recommendation systems by injecting false information, such as deliberately inflating the citation count of their own papers, to obtain favorable recommendations and ratings. This form of attack, commonly termed “shilling attack”, is not only highly concealed but also has an unimaginable impact on all scientific research. To address this problem, we theoretically reveal the impact of shilling attacks on citation recommendation and propose three feasible resistance strategies: historical collaborations, significant citations and content constraints. Based on these insights, we introduce RSA-CR, a robust and hybrid citation recommendation algorithm resistant to shilling attacks. The algorithm constructs a two-layer academic graph and uses random and content generation strategies to initialize author and paper embeddings. Confidence-guided inductive aggregations based on collaboration and citation relationships are then performed at the author and paper sides, where author aggregation results directly influences the paper aggregation strength. Finally, recommendations are made by measuring the distances between the fused paper embeddings. The entire learning process resembles a dumbbell, hence termed “dumbbell inductive learning”. Experiments on four academic datasets demonstrate that our method outperforms baselines in both effectiveness and robustness.

Unsupervised industrial anomaly detection requires accurately identifying defects without labeled data. Traditional autoencoder-based methods often struggle with incomplete anomaly suppression and loss of fine details, as their single-pass decoding fails to effectively handle anomalies with varying severity and scale. We propose a recursive architecture for autoencoder (RcAE), which performs reconstruction iteratively to progressively suppress anomalies while refining normal structures. Unlike traditional single-pass models, this recursive design naturally produces a sequence of reconstructions, progressively exposing suppressed abnormal patterns. To leverage this reconstruction dynamics, we introduce a Cross Recursion Detection (CRD) module that tracks inconsistencies across recursion steps, enhancing detection of both subtle and large-scale anomalies. Additionally, we incorporate a Detail Preservation Network (DPN) to recover high-frequency textures typically lost during reconstruction. Extensive experiments demonstrate that our method significantly outperforms existing non-diffusion methods, and achieves performance on par with recent diffusion models with only 10% of their parameters and offering substantially faster inference. These results highlight the practicality and efficiency of our approach for real-world applications.

Symmetric searchable encryption (SSE) for geo-textual data has attracted significant attention. However, existing schemes rely on task-specific, incompatible indices for isolated specific secure queries (e.g., range or k-nearest neighbor spatial-keyword queries), limiting practicality due to prohibitive multi-index overhead. To address this, we propose RISK, a model for rich spatial-keyword queries on encrypted geo-textual data. In a textual-first-then-spatial manner, RISK is built on a novel k-nearest neighbor quadtree (kQ-tree) that embeds representative and regional nearest neighbors, with the kQ-tree further encrypted using standard cryptographic tools (e.g., keyed hash functions and symmetric encryption). Overall, RISK seamlessly supports both secure range and k-nearest neighbor queries, is provably secure under IND-CKA2 model, and extensible to multi-party scenarios and dynamic updates. Experiments on three real-world and one synthetic datasets show that RISK outperforms state-of-the-art methods by at least 0.5 and 4 orders of magnitude in response time for 1% range queries and 10-nearest neighbor queries, respectively.

Shared-nothing geo-distributed SQL databases, such as CockroachDB, are increasingly vital for enterprise applications requiring data resilience and locality. However, we encountered significant performance degradation at the customer side, especially when their deployments span multiple data centers over a Wide Area Network (WAN). Our investigation identifies the bottleneck in the performance of the Distributed Hash Join (Dist-HJ) algorithm, which is contingent upon a crucial balance between communication overhead and computational load. This balance is severely disrupted when processing skewed data from real-world customer workloads, leading to the observed performance decline. To tackle this challenge, we introduce Bala-Join, an adaptive solution to balance the computation and network load in Dist-HJ execution. Our approach consists of the Balanced Partition and Partial Replication (BPPR) algorithm and a distributed online skewed join key detector. The former achieves balanced redistribution of skewed data through a multicast mechanism to improve computational performance and reduce network overhead. The latter provides real-time skewed join key information tailored to BPPR. Furthermore, an Active-Signaling and Asynchronous-Pulling (ASAP) mechanism is incorporated to enable efficient, real-time synchronization between the detector and the redistribution process with minimal overhead. Empirical study shows that Bala-Join outperforms the popular Dist-HJ solutions, increasing throughput by 25%-61%.

Entity alignment (EA) is to identify equivalent entities across different knowledge graphs (KGs), which can help fuse these KGs into a more comprehensive one. Previous EA methods mainly focus on aligning a pair of KGs, and to the best of our knowledge, no existing EA method considers aligning multiple (more than two) KGs. To fill this research gap, in this work, we study a novel problem of aligning multiple KGs and propose an effective framework named MultiEA to solve the problem. First, we embed the entities of all the candidate KGs into a common feature space by a shared KG encoder. Then, we explore three alignment strategies to minimize the distances among pre-aligned entities. In particular, we propose an innovative inference enhancement technique to improve the alignment performance by incorporating high-order similarities. Finally, to verify the effectiveness of MultiEA, we construct two new real-world benchmark datasets and conduct extensive experiments on them. The results show that our MultiEA can effectively and efficiently align multiple KGs in a single pass. We release the source codes of MultiEA at: https://github.com/kepsail/MultiEA.

In smart grids, smart meters periodically collect users' fine-grained multi-dimensional energy data, which poses great concerns on users' privacy and security. Existing privacy preserving multi-dimensional aggregation schemes suffer from heavy computational burdens, especially for smart meters with limited computational resources. To address these limitations, in this paper we propose an efficient and flexible multi-dimensional data aggregation scheme called MDA-SMuSha, by which smart meters employ the Shamir's multi-secret sharing to generate a set of shared secrets, with the first one kept locally, while the remained ones are packaged and then uploaded to a control center via an aggregator. By the MDA-SMuSha scheme, aggregation results of smart meters' multi-dimensional energy data during multiple periods can be obtained, with only one time of Paillier encryption conducted on the smart meters. In addition, it allows the control center to send query requests flexibly, i.e., at a pre-specified frequency or whenever it wants to obtain statistical data of interests. Rigorous security analyses show that the MDA-SMuSha scheme satisfies security requirements of privacy-preservation, authenticity and data integrity as well as fault-tolerance. Both theoretical analyses and experiment results show that the MDA-SMuSha scheme outperforms state-of-the art methods in terms of computation costs, with comparable communication costs.

Visual text generation, which aims to generate photo-realistic images with coherent and well-formed scene text being rendered, has attracted widespread attention. Although recent works have achieved promising performance, the limited flexibility and controllability hinder their practical applications. We observe that different from natural objects, visual text in real scenes often has an arbitrarily shaped structure with different granularities (i.e., character, word, or line). In this paper, we consider the modality gap between image and text, and propose a new separation and composition pipeline for flexible and controllable visual text generation from only text prompts. At the core of our framework is a novel Hierarchical and Directional Layout representation, i.e., HDLayout, which can model the sequential and multi-granularity nature of the visual text. Under this formulation, we are able to generate arbitrarily shaped visual text automatically. Extensive experiments demonstrate that our method outperforms several strong baselines in a variety of scenarios both qualitatively and quantitatively, yielding state-of-the-art performances on arbitrarily shaped visual text generation.

Mining multiple longest common subsequences (MLCS) from a set of sequences of three or more over a finite alphabet$\Sigma$(a classical NP-hard problem [1]) is an important task in many fields, e.g., bio-informatics, computational genomics, pattern recognition, information extraction, etc. Applications in these fields often involve generating very long sequences (length$\geq 10_{,}000)$, referred to as big sequences. However, both existing exact and approximate MLCS algorithms face severe challenges in handling big sequences due to the over-whelming size of their problem-solving graph model MLCS­-$DAG$(Directed Acyclic Graph), leading to the issue of memory explosion or extremely high time complexity.

In the era of cloud computing and AI, data owners outsource ubiquitous vectors to the cloud, which furnish approx-imate k-nearest neighbors (k-ANNS) services to users. To protect data privacy against the untrusted server, privacy-preserving k-ANNS (PP-ANNS) on vectors has been a fundamental and urgent problem. However, existing PP-ANNS solutions fall short of meeting the requirements of data privacy, efficiency, accuracy, and minimal user involvement concurrently. To tackle this challenge, we introduce a novel solution that primarily executes PP-ANNS on a single cloud server to avoid the heavy communication overhead between the cloud and the user. To ensure data privacy, we introduce a novel encryption method named distance comparison encryption, facilitating secure, efficient, and exact distance comparisons. To optimize the trade-off between data privacy and search performance, we design a privacy-preserving index that combines the state-of-the-art k-ANNS method with an approximate distance computation method. Then, we devise a search method using a filter-and - refine strategy based on the index. Moreover, we provide the security analysis of our solution and conduct extensive experiments to demonstrate its superiority over existing solutions. Based on our experimental results, our method accelerates PP-ANNS by up to 3 orders of magnitude compared to state-of-the-art methods, while not compromising the accuracy.

Patch-wise Transformer based time series forecasting achieves superior accuracy. However, this superiority relies heavily on intricate model design with massive parameters, rendering both training and inference expensive, thus preventing their deployments on edge devices with limited resources and low latency requirements. In addition, existing methods often work in an autoregressive manner, which take into account only historical values, but ignore valuable, easy-to-obtain context information, such as weather forecasts, date and time of day. To contend with the two limitations, we propose LiPFormer, a novel Lightweight Patch-wise Transformer with weak data enriching. First, to simplify the Transformer backbone, LiPFormer employs a novel lightweight cross-patch attention and a linear transformationbased attention to eliminate Layer Normalization and Feed Forward Network, two heavy components in existing Transformers. Second, we propose a lightweight, weak data enriching module to provide additional, valuable weak supervision to the training. It enhances forecasting accuracy without significantly increasing model complexity as it does not involve expensive, human-labeling but using easily accessible context information. This facilitates the weak data enriching to plug-and-play on existing models. Extensive experiments on nine benchmark time series datasets demonstrate that LiPFormer outperforms state-of-the-art methods in accuracy, while significantly reducing parameter scale, training duration, and GPU memory usage. Deployment on an edge device reveals that LiPFormer takes only 1/3 inference time compared to classic Transformers. In addition, we demonstrate that the weak data enriching can integrate seamlessly into various Transformer based models to enhance their accuracy, suggesting its generality.

Collective Location Selection (CLS) aims to identify$k$optimal sites for facility establishment to collectively maximize user attraction. Traditional CLS approaches often overlook user mobility and inter-facility competition, critical factors in real-world scenarios. This paper introduces MC2LS, the first effort on CLS that addresses these gaps by considering user mobility and peer competition. Solving MC2LS is nontrivial due to its NP-hardness. To overcome the challenge of pruning multi-point users with highly overlapping minimum boundary rectangles (MBRs), we develop a position count threshold and two square-based pruning rules. We propose IQuad-tree, a user-MBR-free index, to benefit the hierarchical and batch-wise properties of the pruning rules. We present an$(1-\frac{1}{e})$-approximate greedy solution to MC2LS, and empirical studies demonstrate the superiority of our proposed solution over the state-of-the-art techniques.

Modern database systems rely on workload forecasting to guide performance tuning, resource allocation, and query optimization. However, evolving query patterns and resource demands make accurate workload forecasting increasingly challenging. Most existing methods separately model query and resource behaviors using simple statistics or single-scale forecasting models, thereby ignoring their inherent correlations and leading to inaccurate modeling of real-world workload behaviors. In practice, real-world workloads show that query and resource behaviors are often interdependent and exhibit temporal dynamics. However, most forecasting methods overlook the inherent correlations between query and resource, as well as the temporal patterns across multi-scale resolutions in real workloads. To address these issues, we propose QuadraFormer, an end-to-end forecasting framework that jointly predicts query and resource usage. First, we introduce a unified workload representation that integrates query and resource information into a structured input, enabling joint modeling of workload across variates. Second, we develop an adaptive multi-scale routing mechanism that selects appropriate temporal resolutions to capture periodic patterns and multi-scale variability. Third, we design a novel Quadra-attention mechanism that models both intra- and inter-patch temporal dependencies as well as intra- and inter-dimensional dependencies. Extensive experiments on real-world workloads show that QuadraFormer consistently outperforms competitive baselines, achieving the highest F1 score of 98.38% and accuracy of 86.17%, with F1 improvements of up to 10.28% over the best-performing baseline, while reducing training time by up to 2.16×.

Shadow characteristics are of great importance for scene understanding. Existing works mainly consider shadow regions as binary masks, often leading to imprecise detection results and suboptimal performance for scene understanding. We demonstrate that such an assumption oversimplifies light-object interactions in the scene, as the scene details under either hard or soft shadows remain visible to a certain degree. Based on this insight, we aim to reformulate the shadow detection paradigm from the opacity perspective, and introduce a new fine-grained shadow detection method. In particular, given an input image, we first propose a shadow opacity augmentation module to generate realistic images with varied shadow opacities. We then introduce a shadow feature separation module to learn the shadow position and opacity representations separately, followed by an opacity mask prediction module that fuses these representations and predicts fine-grained shadow detection results. In addition, we construct a new dataset with opacity-annotated shadow masks across varied scenarios. Extensive experiments demonstrate that our method outperforms the baselines qualitatively and quantitatively, enhancing a wide range of applications, including shadow removal, shadow editing, and 3D reconstruction.

Abstract This study introduces an innovative framework named scale for processing dynamic skyline queries securely in cloud environments. Unlike previous approaches that require complex operations on encrypted data, scale simplifies dynamic skyline domination to mere comparisons, significantly improving query efficiency. Through empirical evaluations over four datasets, we show that scale accelerates query processing nearly 1000-fold compared to existing state-of-the-art methods. Specifically, scale shows significant efficiency improvements by simplifying query interactions to a single round between the user and the cloud, which is validated through empirical studies on multiple datasets. Moreover, we introduce two distributed versions of scale , dist-scale-s and dist-scale-e , which further optimize performance by facilitating parallel processing. This adaptation showcases a substantial reduction in response times and computational overhead, underpinning the scalability and effectiveness of our framework in handling large-scale, secure cloud-based queries.

Federated Learning (FL) has become a state-of-the-art technique for addressing data isolation and privacy problems. However, the traditional FL framework has limitations on lack of labeled data, adaptation to evolving environments and tasks, and insufficient generalization of the global model due to non-independent and identically distributed (non-IID) data. These issues suggest incorporating human knowledge and interaction into the FL workflow can be beneficial. Human-machine hybrid intelligence is an area that human abilities are considered to prompt the usability and robustness of the system by providing human domain knowledge. Combining FL and human-machine hybrid intelligence can fully utilize their benefits and complement each other perfectly. This article presents our vision of the next generation of FL, Human-Machine Hybrid Federated Learning, namely HIFL. And this work first defines the concept of HIFL and proposes three patterns of collaboration for HIFL: (1) local hybrid intelligence (LocalHIFL), (2) separate hybrid intelligence (SeparateHIFL), and (3) cross hybrid intelligence (CrossHIFL). In each pattern, we survey methodologies and techniques that are utilized to address specific problems that occurred after adding human-machine collaboration in FL process. Besides, we exhibit some potential application scenarios, and provide several open challenges and opportunities contained in HIFL. This survey intends to provide a high-level summarization for improving FL by combining human-machine hybrid intelligence, and to motivate interested readers to consider approaches for designing effective FL approaches and ways of intelligence fusion according to their requirements.

Cloud-based quantization is a key technique for deploying deep neural networks on resource-constrained devices. However, the growing number of heterogeneous devices has placed an increasing burden on the cloud server. It is necessary to handle the high costs of quantizing original models to multiple bitwidths on the cloud server. Therefore, we propose an efficient hierarchical quantization method (HQAQ), which transforms classical cloud-based quantization to two-phase quantization: cloud-edge post-training quantization and edge-device hybrid re-quantization. HQAQ first quantizes original models on the cloud server and distributes the quantized model to edge servers. Then the edge servers re-quantize the quantized model to respond to heterogeneous devices’ requests. To minimize the computational costs on edge servers, edge-device hybrid re-quantization integrates top-bitwidth quantization-aware training with cross-bitwidth quantization points transfer. Quantization points transfer employs clustering to discover the distribution of the quantization points with higher bit-width, which helps quantize the models of lower-bitwidth models. The experimental results on image classification and time series prediction tasks demonstrate that the proposed method improves communication efficiency while maintaining model performance.

Ensuring the ACID properties of transactions is the fundamental functionality of transactional DBMSs. However, through our study on existing solutions on transaction management, we found that transaction implementations in some mainstream databases, such as MySQL, MariaDB and TiDB, may violate what they claim in their documentation, in the form of incorrect database state or query results. Since there is still a lack of efficient and comprehensive testing methods to detect bugs within transaction management implementation for off-the-shelf DBMSs at present, we propose Fucci, a fuzzing framework, to solve the problem. Given a target DBMS, Fucci improves the efficiency of detecting transaction bugs through three key components: Random Conflict Construction (RCC), Multilevel Constraint Solving (MCS), and Experience-driven Automatic Simplification (EAS). RCC addresses the issue of inadequate case validity by ensuring the presence of read-write or write-write conflicts between transactions. MCS enhances the accuracy and efficiency of the transaction oracle by employing an external multi-version control system to solve data visibility. EAS is ultimately adopted to improve the efficiency of simplification and the readability of the identified bug cases. All of the above strategies are tested on commercial databases such as MySQL, MariaDB and TiDB. Accordingly, 6 previously unknown transaction bugs and 14 known duplicate transaction bugs have been newly discovered, most of which have been officially acknowledged.

Due to the increasing demand for extreme-scale time-series data workloads in data centers, it is required to build a high-performance semantic caching system that leverages the semantics and results of historical queries to answer time-series queries. Existing caching solutions either ignore the semantics of queries, offering suboptimal performance, or focus only on specific scenarios, providing small-capacity, limited functionality. In this paper, we summarize the query patterns of time-series data workload and propose the definition of semantic time-series caching for the first time. Accordingly, we present a semantic time-series caching system, STsCache, based on a hybrid storage model with memory and NVMe SSD. We propose a series of optimized strategies, such as slab-based semantic data management, semantic index, semantic value-driven batch eviction, time-aware deduplication insertion, and lazy compaction. We implemented and evaluated STsCache via benchmarks and production environments. STsCache can increase throughput of popular time-series databases (InfluxDB, TimescaleDB) by 4.8–10.8X and reduce latency by 79.9%-93.5%. Compared with the latest time-series caching schemes (TSCache, BSCache), STsCache can increase throughput by 1.5–4.5X, reduce latency by 59.4%-81.9%, and increase hit ratios by 22.5%-82.4%.

Proximity graphs (PG) have gained increasing popularity as the state-of-the-art solutions to k -approximate nearest neighbor ( k -ANN) search on high-dimensional data, which serves as a fundamental function in various fields, e.g., retrieval-augmented generation. Although PG-based approaches have the best k -ANN search performance, their index construction cost is superlinear to the number of points. Such superlinear cost substantially limits their scalability in the era of big data. Hence, the goal of this paper is to accelerate the construction of PG-based methods without compromising their k -ANN search performance. To achieve this goal, two mainstream categories of PG are revisited: relative neighborhood graph (RNG) and navigable small world graph (NSWG). By revisiting their construction process, we find the issues of construction efficiency. To address these issues, we propose a new construction framework with a novel pruning strategy for edge selection, which accelerates RNG construction while keeping its k -ANN search performance. Then, we integrate this framework into NSWG construction to enhance both the construction efficiency and k -ANN search performance of NSWG. Extensive experiments are conducted to validate our construction framework for both RNG and NSWG, and that it significantly reduces the PG construction cost, achieving up to 5.6x speedup, while not compromising the k -ANN search performance.

Mining multiple longest common subsequences (MLCS) from a set of sequences of length three or more over a finite alphabet (a classical NP-hard problem) is an important task in many fields, e.g., bioinformatics, computational genomics, pattern recognition, information extraction, etc. Applications in these fields often involve generating very long sequences (length$\geqslant$10,000), referred to as big sequences. Despite efforts in improving the time and space complexities ofMLCSmining algorithms, both existing exact and approximate algorithms face challenges in handling big sequences due to the overwhelming size of their problem-solving graph modelMLCS-DAG(DirectedAcyclicGraph), leading to the issue of memory explosion or extremely high time complexity. To bridge the gap, this paper first proposes a new identification and deletion strategy for different classes of non-critical points in the mining ofMLCS, which are the points that do not contribute to theirMLCSs mining in theMLCS-DAG. It then proposes a newMLCSproblem-solving graph model, namely$DAG_{KP}$(a newMLCS-DAGcontaining onlyKeyPoints). A novel parallelMLCSalgorithm, calledKP-MLCS(KeyPoint basedMLCS), is also presented, which can mine and compress allMLCSs of big sequences effectively and efficiently. Extensive experiments on both synthetic and real-world biological sequences show that the proposed algorithmKP-MLCSdrastically outperforms the existing state-of-the-artMLCSalgorithms in terms of both efficiency and effectiveness.

Collective Location Selection (CLS) has received significant research attention in the spatial database community due to its wide range of applications. The CLS problem selects a group ofkpreferred locations among candidate sites to establish facilities, aimed at collectively attracting the maximum number of users. Existing studies commonly assume every user is located in a fixed position, without considering the competition between peer facilities. Unfortunately, in real markets, users are mobile and choose to patronize from a host of competitors, making traditional techniques unavailable. To this end, this paper presents the first effort on a CLS problem in competition scenarios, calledmc$^{2}$2ls, taking into account the mobility factor. Solvingmc$^{2}$2lsis a non-trivial task due to its NP-hardness. To overcome the challenge of pruning multi-point users with highly overlapped minimum boundary rectangles (MBRs), we exploit a position count threshold and design two square-based pruning rules. We introduce IQuad-tree, a user-MBR-free index, to benefit the hierarchical and batch-wise properties of the pruning rules. We propose an$(1-\frac{1}{e})$-approximate greedy solution tomc$^{2}$2lsand incorporate a candidate-pruning strategy to further accelerate the computation for handling skewed datasets. Extensive experiments are conducted on real datasets, demonstrating the superiority of our proposed pruning rules and solution compared to the state-of-the-art techniques.

As device-free identity recognition (IR) gains popularity and the demand for the Internet of Things (IoT) continues to grow, a new-era IR system featuring multiple distributed recognition devices and edge servers faces two main challenges: model adaptability and balancing the personalization of devices with the generalization of the system. This research introducesFedHMIR, a federated framework designed to simultaneously address these challenges by harmonizing human-machine collaboration with personalization-generalization trade-offs. The proposed framework features a human-machine cooperative online internal update mechanism, leveraging reinforcement learning to maintain the adaptability of personalized local IR models. To counter overfitting and enhance the generalization of the overall IR system, an external update process incorporating a confidence index is introduced. Additionally, the framework employs asynchronous internal and external update procedures to effectively balance personalization and generalization between local and global models. Finally, extensive experiments on three diverse real-world datasets demonstrate the effectiveness and advantages ofFedHMIRcompared to state-of-the-art baselines.

Federated learning (FL) learns an optimal global model by aggregating local models trained on distributed data from different devices. Due to heterogeneous data distributions across devices, local models will be divergent, resulting in the global model’s performance degradation. Recent studies attempt to balance local models to obtain a global model that can adapt to each device. But they ignore a more challenging problem that redundant local models from devices will break the balance, resulting in the global model overfitting redundant local models. Therefore, we propose FedSmooth, a novel global aggregation algorithm. FedSmooth first identifies the redundant local models without sensitive local information (e.g., label distribution), then designs a smooth global aggregation to strengthen the effect of local models that can accelerate finding the optimal global model. Experimental results show that our method outperforms 4 SOTA baseline methods even if there is more redundancy.

In database management systems, concurrency control manages the interleaved execution of multiple transactions, ensuring data integrity and consistency. However, in high-conflict scenarios, current strategies often lead to frequent transaction aborts, resulting in a significant waste of time on ineffective operations. To effectively address this challenge, we introduce an innovative Prior Cascading Concurrency Control (PC3) mechanism. This mechanism aims to proactively predict conflicts and minimize the performance penalty caused by these conflicts through a series of precise decisions. Specifically, PC3employs various prediction models to forecast transaction working sets, providing accurate transaction information for conflict detection. On this basis, we implemented a hash-based conflict detection method and established a cascading decision algorithm to minimize transaction abort frequency. Experimental results on the TPC-C workload show that in high-conflict scenarios with a Zipfian skew and thread counts between 5 and 40, PC3reduces the number of erroneous transactions by 18 times, and increases throughput by approximately 30.7%. compared to the best-performing optimistic methods.

In the era of the Internet of Things (IoT), the rapid development of cloud computing has been advancing location-based services (LBS). To enjoy the considerable advantages of lower cost and higher performance of cloud computing, it has become the first choice for most IoT enterprises to outsource data and services to public clouds. However, the privacy protection of data and the integrity of query results cannot be effectively guaranteed since public clouds cannot be fully trusted. In this paper, we propose a lightweight verifiable range query scheme (namely VeriRange). First, a pair of mutually perpendicular locality-sensitive hashing (LSH) is adopted to divide the original geographic data into subsets. Then a pivoted k dimensional (PKD) tree and a height-balanced binomial search tree (i.e., AVL tree) are established on the client and the cloud respectively to accelerate the processes of queries. To ensure the verification of query results, a keyed hash function is utilized to generate a verification tag for each subset. Verification tags in the public cloud are encrypted and independent of queries. It ensures fast verification of query results because the verification tags are not repeatedly calculated on each query. Formal security analysis shows VeriRange ensures the privacy of data, queries, and results. Experimental studies were conducted on real and synthetic datasets and demonstrated that the query and verification time in VeriRange is almost 2-3 orders of magnitude faster than that in state-of-art schemes.

Deep learning inference has become a fundamental component of cloud service providers, while privacy issues during services have received significant attention. Although many privacy-preserving schemes have been proposed, they require further improvement. In this article, we proposeSerpens, an efficient convolutional neural network (CNN) secure inference framework to protect users’ uploaded data. We introduce a pair of novel concepts, namely separable and conditional separable, to determine whether a layer in CNNs can be computed over multiple servers or not. We demonstrate that linear layers are separable and construct factor-functions to reduce their overhead to nearly zero. For the two nonlinear layers, i.e., ReLU and max pooling, we design four secure protocols based on homomorphic encryption and random masks for two- and n-server settings. These protocols are essentially different from existing schemes, which are primarily based on garbled circuits. In addition, we extensively propose a method to split the image securely. The experimental results demonstrate thatSerpensis$60\times -197\times$faster than the previous scheme in the two-server setting. The superiority ofSerpensis even more significant in the n-server setting, only less than an order of magnitude slower than performing plaintext inference over clouds.

Spatial temporal forecasting of urban sensors is essentially important for many urban systems, such as intelligent transportation and smart cities. However, due to the problem of hardware failure or network failure, there are some missing values or missing monitoring sensors that need to be interpolated. Recent research on deep learning has made substantial progress on imputation problem, especially temporal aspect (i.e., time series imputation), while little attention has been paid to spatial aspect (both dynamic and static) and long-term temporal dependencies. In this article, we proposed a spatial temporal imputation model, named Long Short-Term Graph Convolution Networks (LSTGCN), which includes gated temporal extraction (GTE) module, multi-head attention-based temporal capture (MHAT) module, long-term periodic temporal encoding (LPTE) module, and bidirectional spatial graph convolution (BSGC) module. The GTE adopts a gated mechanism to filter short-term temporal information, while the MHAT utilizes position encoding to enhance the difference of each timestamps, then use multi-head attention to capture short-term temporal dependency. The BSGC is adopted to handle with spatial relationships between sensor nodes. And we design a periodic encoding technique to process long-term temporal dependencies. The BSGC handles spatial relationships between sensor nodes, and a periodic encoding technique is used to process long-term temporal dependencies. Our experimental analysis includes completion and forecasting tasks, as well as transfer and ablation analyses. The results show that our proposed model outperforms state-of-the-art baselines on real-world datasets.

Temporal recommendation systems aim to suggest items to users at the optimal time. However, the significant imbalance of items in the training data poses a major challenge to predictive accuracy. Existing approaches attempt to alleviate this issue by modifying the loss function or utilizing resampling techniques, but such approaches may inadvertently amplify the specificity of certain behaviors.

Star-join query is the fundamental task in data warehouse and has wide applications in On-line Analytical Processing (olap) scenarios. Due to the large number of foreign key constraints and the asymmetric effect in the neighboring instance between the fact and dimension tables, even those latest dp efforts specifically designed for join, if directly applied to star-join query, will suffer from extremely large estimation errors and expensive computational cost. In this paper, we are thus motivated to propose DP-starJ, a novel Differentially Private framework for star-Join queries. DP-starJ consists of a series of strategies tailored to specific features of star-join, including 1) we unveil the different effects of fact and dimension tables on the neighboring database instances, and accordingly revisit the definitions tailored to different cases of star-join; 2) we propose Predicate Mechanism (PM), which utilizes predicate perturbation to inject noise into the join procedure instead of the results; 3) to further boost the robust performance, we propose a dp-compliant star-join algorithm for various types of star-join tasks based on PM. We provide both theoretical analysis and empirical study, which demonstrate the superiority of the proposed methods over the state-of-the-art solutions in terms of accuracy, efficiency, and scalability.

In various multi-party cooperations data stored on blockchains (i.e., on-chain data) should be decentralized consistent, verifiable, traceable, and immutable. Online analytical processing (OLAP) services are critical requirements in these applications. However, OLAP performances of existing blockchain systems are much worse than those of relational databases due to the lack of SQL support. In this paper, we propose a novel SQL-empowered analytical blockchain framework, aChain. It fully provides SQL-based OLAP services, while keeping the secure characteristics. Specifically, aChain relationally reorganizes on-chain data to support full SQL executions. Then, a relational versioning scheme is designed to ensure the atomicity and consistency of transactions. Furthermore, SQL-based APIs are designed based on an execute-order-validate architecture. Finally, we demonstrate that the performance of aChain and MySQL (in both cluster and non-cluster models) is at the same level on a typical OLAP benchmark, TPC-H.

Since its introduction in 2003, the influence maximization (IM) problem has drawn significant research attention in the literature. The aim of IM, which is NP-hard, is to select a set of$k$users known as seed users who can influence the most individuals in the social network. The state-of-the-art algorithms estimate the expected influence of nodes based on sampled diffusion paths. As the number of required samples has been recently proven to be lower bounded by a particular threshold that presets tradeoff between the accuracy and the efficiency, the result quality of these traditional solutions is hard to be further improved without sacrificing efficiency. In this article, we present an orthogonal and novel paradigm to address the IM problem by leveraging deep reinforcement learning (RL) to estimate the expected influence. In particular, we present a novel framework called deeP reInforcement leArning-based iNfluence maximizatiOn (PIANO) that incorporates network embedding and RL techniques to address this problem. In order to make it practical, we further present PIANO-E and PIANO$\text{@}\langle d\rangle $, both of which can be applied directly to answer IM without training the model from scratch. Experimental study on real-world networks demonstrates that PIANO achieves the best performance with respect to efficiency and influence spread quality compared to state-of-the-art classical solutions. We also demonstrate that the learned parametric models generalize well across different networks. Besides, we provide a pool of pretrained PIANO models such that any IM task can be addressed by directly applying a model from the pool without training over the targeted network.

Transportation networks are essential to the operation of societies and economies. Protecting the privacy of sensitive information is a meaningful conception in sustainable transport when mining the transportation data. In data mining, differential privacy (DP) has provable privacy guarantees for releasing sensitive data by introducing randomness into query results. However, it suffers from significant accuracy loss of outputs when the query has high sensitivity (e.g., triangle counting). The reason is that the range of random perturbation to each query result in DP is too large. It consists of all possible output values for a query that forms a large or even unbounded interval. However, when impose perturbation only in a small neighborhood of the true query result, the similarity measure based on randomness in DP fails. Thereupon, we introduce fuzziness into DP to formulate new models which have smaller disturbance via fuzzy similarity measures. In this article, we establish a novel and general theory of private data analysis, fuzzy differential privacy (FDP). The new theory FDP aims to acquire a more flexible tradeoff between the accuracy of outputs and the privacy-preserving level of data. FDP combines DP with fuzzy set theory by introducing fuzziness into the query results and characterizing similarities between outputs via multiple fuzzy similarity measures. From this perspective, DP can be viewed as a special case of FDP with probabilistic similarity measure. Compared with DP, FDP has three superiorities: 1) most fuzzy similarity measures in FDP support sliding window perturbation strategies we proposed, which refer to perturbation in a small neighborhood of the query results; 2) FDP adds noise to the query results only according to a fraction of all possible neighboring datasets; and 3) the fuzzy similarity with valued in [0,1] quantifies the privacy protection level intuitively. These three points enable more accurate outputs while providing provable and intuitive privacy guarantees. As for subgraph counting, the state-of-the-art method is ladder framework in DP. We illustrate FDP mechanisms by applying them to a common application in subgraph counting–triangle/4-cliques counting. Experiments show that FDP is effective and efficient with smaller output errors than DP.

In smart grids, various Internet-of-Things-based (IoT-based) components are massively deployed across the power systems. However, most of these IoT-based components have their own vulnerabilities, leveraging which malicious users can launch different cyber/physical attacks to steal electricity. Economic losses caused by electricity theft amount to $96 billion in 2017. Most existing electricity theft detection techniques suffer from either a high deployment cost or a low detection accuracy. To address these concerns, we propose a novel Electricity Theft Detector based upon Convolutional Long Short Term Memory neural networks, called ETD-ConvLSTM. By installing a central observer meter in each community, we can know which communities have malicious users. For these communities, users’ time series of electricity consumptions with temporal correlations are transformed into spatio-temporal sequence data, mainly by constructing a two-dimensional matrix containing both consumptions and consumption differences among several adjacent days. This matrix is then divided into a sequence of sub-matrices, which are then fed into a ConvLSTM network consisting of multiple stacked ConvLSTM layers, with each layer formed by several temporarily concatenated ConvLSTM nodes. When capturing the periodicity in users’ consumption patterns, the ETD-ConvLSTM method considers both global and local knowledge, and hence the detection accuracy improves significantly. Simulations results show that compared with existing state-of-the-art detectors, the proposed ETD-ConvLSTM method can obtain better or comparable performance in terms of detection accuracy, false negative rates and false positive rates within much shorter detection time.

Facility Relocation (FR), which is an effort to reallocate the placement of facilities to adapt to the changes of urban planning, has remarkable impact on many areas. Existing solutions fail to guarantee the result quality on relocating k > 1 facilities. As k -FR problem is NP-complete and is not submodular or non-decreasing, traditional greedy algorithm cannot be directly applied. We propose to transform k -FR into another facility placement problem, which is submodular and non-decreasing. We prove that the optimal solutions of both problems are equivalent. Accordingly, we present the first approximate solution toward the k -FR, FR2FP. Our extensive comparison over both FR2FP and the state-of-the-art solution shows that FR2FP, although it provides approximation guarantee, cannot necessarily given superior results. The comparison motivates us to present an advanced approximate solution, FR2FP-ex. Moreover, based on Lagrangian relaxation, we develop an algorithm that can adjust the approximation ratio. Extensive experiments verified that, FR2FP-ex demonstrates the best result quality, and it is very close to the optimal solution. In addition, we also unveil the scenarios when the state-of-the-art would fail. We further generalize the k -FR problem, considering the budget for relocation and the cost of each facility. We also present corresponding approximate solutions toward the new problem and prove the approximation ratio.

Traffic prediction is the core task of intelligent transportation system (ITS) and accurate traffic prediction can greatly improve the utilization of public resources. Dynamic interaction of multiple spatial relationships will influence the accuracy of traffic prediction. However, many existing methods only consider static spatial relationships, which restricts the accuracy of the prediction. To address the above problem, in this article, we propose the Dynamic Multi-Graph Fusion Network (DMGF-Net) to model the spatial-temporal correlations in traffic network. In the DMGF-Net, the fusion graph is designed to leverage and extract the various spatial correlations between different regions by fusing spatial graph, semantic graph, and spatial-semantic graph. Further, to dynamically learn the importance of different neighbors, we design the Dynamic Spatial-Temporal Unit (DSTU), which can adjust the aggregation weights of different neighbors by combining the convolution operation and the attention mechanism. It can selectively aggregate spatial-temporal features from different neighbors. Extensive experiments on three datasets demonstrate that effectiveness of our model, especially on PEMS08, our model achieves an increase of about 8.55% and 7.55% in terms of MAE and RMSE than the static model STGCN.

Graph Convolutional Network (GCN) has achieved extraordinary success in learning representations of nodes in graphs. However, regarding Heterogeneous Information Network (HIN), existing HIN-oriented GCN methods still suffer from two deficiencies: (1) they cannot flexibly explore all possible meta-paths and extract the most useful ones for each target object, which hinders both effectiveness and interpretability; (2) before performing aggregation, they often require some additional time-consuming pre-processing operations, which increase the computational complexity. To address the above issues, we propose an interpretable and efficient Heterogeneous Graph Convolutional Network (ie-HGCN) to learn the representations of objects in HINs. It is designed as a hierarchical aggregation architecture, i.e., object-level aggregation and type-level aggregation. The new architecture can automatically evaluate all possible meta-paths within a length limit, and discover and exploit the most useful ones for each target object, i.e., at fine granularity. It also reduces the computational cost by avoiding additional time-consuming pre-processing operations. Theoretical analysis shows its ability to evaluate the usefulness of all possible meta-paths, its connection to the spectral graph convolution on HINs, and its quasi-linear time complexity. Extensive experiments on four real network datasets demonstrate its interpretability, efficiency as well as its superiority against thirteen baselines.

Hierarchical count histograms is the task of publishing count statistics at different granularity as per hierarchy defined on a dimension table in a data warehouse, which has wide applications in On-line Analytical Processing (OLAP) scenarios. In this paper, we systematically investigate this task subjected to the rigorous privacy-preserving constraint under the untrusted server setting. Our study first reveals that the straightforward baseline approach of the local differential privacy fails to achieve a satisfactory privacy and utility tradeoff. We are thus motivated to propose DP-HORUS, a novel crypto-assisted Differentially Private framework for Hierarchical cOunt histogRams under Untrusted Server. DP-HORUS consists of a series of novel designs, including 1) Encrypted Hierarchical Tree (EHT) structure, which maintains the concept hierarchy in the input data; 2) Random Matrix (RM), which reduces communication and computational cost; 3) To further boosted the utility, we propose DP-HORUS+ encompassing two additional modules of Histograms Structure (HS) and Hierarchical Consistency (HC), which are respectively introduced to reduce the noise caused by data sparsity and to ensure the hierarchy consistency. We provide both theoretical analysis and extensive empirical study on both real-world and synthetic datasets, which demonstrates the superior utility of the proposed methods over the state-of-the-art solutions while ensuring strict privacy guarantee.

Given an sql, a rdbms performs a series of operations to generate a Query Execution Plan (qep), which tells how the results will be collected and returned eventually. The whole pipeline for obtaining the qep is the core functionality of a rdbms, thus is definitely a fundamental knowledge that must be acquired by database learners and junior engineers of any rdbms. Unfortunately, though the majority of rdbmss provide EXPLAIN statement to show the qep, general users cannot see how/why these plans are generated. The only way for learning that is to turn to the textbook, which contains limited number of predefined examples accordingly. However, they are too sketchy to allow us to have a hand-on experience in practice. In this work, we present a general framework, DBinsight, that unveils the query processing pipeline visually at each phase during the processing pipeline, including parsing, translating, query optimization, etc. Considering that the underlying designs and optimization strategies of rdbmss are different, in DBinsight we present an SQPProfile interface, such that heterogeneous data structures in various rdbmss are normalized to a uniform format. Thanks to that, in DBinsight we only need to focus on offering the presentation and interaction functionalities based on the uniformed SQPProfile, and do not need to worry about the difference in the underlying rdbmss.

With the extensive usage of convolutional neural networks (CNNs), privacy issues within practical applications have attracted much attention, especially when deep learning services are provided by third-party clouds. Many private inference schemes have been proposed, but their overheads are still too large. In this work, we find that the inference procedure of CNNs can be separated and performed synergistically by many parties. Following this observation, we present a pair of novel notions, namely separable and conditional separable, to tell whether a layer in CNNs can be exactly computed over multiple parties or not. Besides, we also prove that CNNs are conditionally separable. Accordingly, we propose Serpens, a private inference framework under multi-server settings. Serpens reduces the overhead of linear layers to almost zero, and now the computing bottleneck is ReLU. To address that, we design two secure ReLU protocols based on homomorphic encryption and random masks for two- and three-server settings. Experimental results show that Serpens is 78x-105x faster than the state-of-the-art private inference scheme in the two-server setting, and the superiority of Serpens is even larger in the three-server setting, only 11x-64x slower than performing the same inference over plaintext images.

Delivery locations are fundamental data source for intelligent logistics, which can be used in route planning, arrival time estimation, parcel allocation, etc. Using the Geocoded way-bill location of an address as the delivery location is not sufficient, due to wrong address parsing, coarse-grained POI database, or different preferences of customers. To mitigate the insufficiency of Geocoding, some methods have been proposed, which utilize couriers' locations when waybills are confirmed to be delivered for delivery location inference. Nevertheless, these methods highly rely on the quality of couriers' annotations and fail when couriers confirm deliveries with delays. We propose to infer actual delivery locations of addresses from couriers' trajectories. This idea lies on an observation that the semantics of delivering a parcel are well captured by couriers' trajectories (e.g., a stay point would be generated when a delivery occurs), which holds even couriers confirm deliveries with delays. Specifically, we design Delivery Location Inference under Mis-Annotation (DLInfMA), which (1)generates location candidates from stay points in couriers' trajectories; (2) extracts features from both an address and its location candidates; and (3) uses an attention-based neural network model LocMatcher to predict the delivery location for each address. Experiments on two real-world datasets from JD Logistics as well as synthetic datasets demonstrate the effectiveness, robustness and scalability of DLInfMA. We also present a deployed system along with two applications based on DLInfMA.

Recently, aside with the prevalent usage of location-based social network, location-aware influence maximization (laim) problem has received plenty of attention in viral marketing. It aims to find a set of seed users such that information propagated from them can reach the largest number of users within particular geographical regions. However, existing solutions to laim can only work on single simple query region, e.g., a rectangle, instead of complex ones. Besides, there is no ready-to-use system for users to address laim visually. In this work, we present a pair of solutions towards location-aware influence maximization problem. Both can work on queries with arbitrary number of regions and arbitrary shapes. More importantly, we implement a web-based system, namely a2RegInf, which enables viral marketers to address laim visually, with native GPU support. To the best of our knowledge, we are the first to provide a ready-to-use system for answering the problem over web-based interface that supports arbitrary number of arbitrary shaped query regions.

For achieving the goal of two-way communication and power flows, smart grids are integrated with much state-of-the-art hardware and software. However, these newly added components also introduce a lot of vulnerabilities into the power systems, which results in that malicious users can launch various cyber–physical attacks to steal electricity. The existing electricity theft detection techniques suffer from an implicit assumption that malicious users tamper with smart meter readings to values much less than their actual electricity consumptions. These are called large-amount electricity theft (LET) attacks. Nevertheless, in the real world, some malicious users may be cautious enough to deliberately launch small-amount electricity theft (SET) attacks, where smart meter readings are manipulated to numbers slightly lower than the actual values, mainly to escape detection. To address this limitation, we propose a detector that is able to deal with both LET and SET attacks effectively. This detector applies a cumulative sum (CUSUM) control chart and a Shewhart control chart together to analyze users’ reported readings and measurements of a central observer meter. It consists of an electricity theft detection phase, which aims to detect the existence of LET/SET attacks timely and a malicious user identification phase, which aims to identify malicious users exactly. Extensive experiments are conducted to evaluate the proposed detector, and the results show that it has good performance in terms of several metrics.

For accommodating rapidly increasing power demands, power systems are transitioning from analog systems to systems with increasing digital control and communications. Although this modernization brings many far-reaching benefits, the hardware and software newly incorporated into the power systems also incur many vulnerabilities. By taking advantage of these vulnerabilities, adversaries can launch various cyber/physical attacks to tamper with electricity meter readings, i.e., to steal electricity. It is reported that total worldwide annual economic losses caused by electricity theft reached up to almost one hundred billion dollars in recent years. With methods to tamper with meter readings becoming more versatile, secret, and flexible, electricity theft tends to get even more serious in modernized power systems. For preventing adversaries from stealing electricity, researchers have done a lot of works. Although some related surveys on these works exist, they are not updated or just discuss electricity theft in a specific region. This survey aims to gain a comprehensive and in-depth understanding of the electricity theft issue. After investigating how adversaries tamper with meter readings, we systematically survey all existing detection methods up to date, which is classified into machine learning- and measurement mismatch-based methods. Adverse effects and political and socioeconomic factors of electricity theft are also provided. This survey can help relevant researchers to shape future research directions, especially in the area of developing new effective electricity theft detection methods.

The ongoing coronavirus disease 2019 (COVID-19) is a pandemic causing millions of deaths, devastating social and economic disruptions. Testing individuals for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen of COVID-19, is critical for mitigating and containing COVID-19. Many countries are implementing group testing strategies against COVID-19 to improve testing capacity and efficiency while saving required workloads and consumables. A group of individuals’ nasopharyngeal/oropharyngeal (NP/OP) swab samples is mixed to conduct one test. However, existing group testing methods neglect the fact that mixing samples usually leads to substantial dilution of viral ribonucleic acid (RNA) of SARS-CoV-2, which seriously impacts the sensitivity of tests. In this paper, we aim to screen individuals infected with COVID-19 with as few tests as possible, under the premise that the sensitivity of tests is high enough. To achieve this goal, we propose an Adaptive Group Testing (AdaGT) method. By collecting information on the number of positive and negative samples that have been identified during the screening process, the AdaGT method can estimate the ratio of positive samples in real-time. Based on this ratio, the AdaGT algorithm adjusts its testing strategy adaptively between an individual testing strategy and a group testing strategy. The group size of the group testing strategy is carefully selected to guarantee that the sensitivity of each test is higher than a predetermined threshold and that this group contains at most one positive sample on average. Theoretical performance analysis on the AdaGT algorithm is provided and then validated in experiments. Experimental results also show that the AdaGT algorithm outperforms existing methods in terms of efficiency and sensitivity.Note to Practitioners—Real-time reverse transcription-polymerase chain reaction (rRT-PCR) tests provide scope for automation and are one of the most widely used laboratory methods for detecting the SARS-CoV-2 virus. This paper is motivated by the following challenges: (1) Many countries are experiencing an acute shortage of professionals and consumables for conducting rRT-PCR tests; (2) Group sizes of existing group testing methods against COVID-19 may not be optimal, which adversely impacts the efficiency of the screening of the SARS-CoV-2 virus; (3) Existing group testing methods do not consider the fact that the sensitivity of rRT-PCR tests usually decreases with the group size. The objective of this paper is to improve the efficiency and sensitivity of large-scale screening against COVID-19. For achieving this goal, we propose an Adaptive Group Testing (AdaGT) algorithm, which has the following advantages: (1) It can improve the efficiency for screening the SARS-CoV-2 virus, mainly by adaptively adjusting its testing strategy between an individual testing strategy and a group testing strategy based upon an estimated ratio of positive samples during the screening process; (2) It can guarantee a high sensitivity of the rRT-PCR tests by determining the group sizes of the group testing strategy based upon some constraints; (3) We derive an appropriate threshold for the estimated ratio of positive samples such that the AdaGT algorithm can achieve a minimum average number of rRT-PCR tests and can be directly employed in practical applications.

Multi-view data effectively model and characterize the underlying complex systems, and multi-view clustering is of great significance for revealing the mechanisms of systems, which groups objects into different clusters with high intra-cluster and low inter-cluster similarity for all views. Current algorithms are criticized for undesirable performance because they solely focus on either the shared features or correlation of objects, failing to address the heterogeneity and structural constraint of various views. To overcome these problems, a novelMulti-viewClustering withSelf-representation andStructuralConstraint (MCSSC) is proposed, which is a network-based method by fusing matrix factorization and low-rank representation of various views. Specifically, to remove heterogeneity of multi-view data, a network is constructed for each view, which casts the multi-view clustering into the multi-layer networks clustering problem. To extract the shared features of multiple views, MCSSC factorizes matrices associated with networks by projecting them into a common space and jointly learns an affinity graph for objects in multiple views with self-representation. To facilitate the clustering, the structural constraint is imposed on the affinity graph, where the clusters are identified. Extensive experiments demonstrate that MCSSC significantly outperforms the state-of-the-art in terms of accuracy, implying that the superiority of the proposed method.

With the increase in popularity of graph structured data arising in different areas such as Web, social network, communication network, knowledge graph, etc., there is a growing need for partitioning and repartitioning large graph data in a distributed system. However, the existing graph repartitioning methods are known for poor efficiency in the distributed environment and most of them lack a balance mechanism between edge cut and load balance. In this article, we introduce a new two-phase method to improve the result of distributed graph repartitioning. We first design a local method to identify all the potential candidate vertices that could improve the graph repartitioning result in load balance and edge cut at once in each partition locally. After that, we propose to migrate the selected vertices among the given initial partitions to improve the result of graph repartitioning. During this procedure, we propose to adopt a synchronous vertex migration method to balance both the edge cuts and load balance problems. Extensive experimental results demonstrate that the proposed method is more efficient than the existing methods in several aspects such as communication cost, running time, edge cut, and load balance. We also run SSSP and PageRank applications based on the graph repartitioning result on Giraph to indicate the efficiency of the proposed method.

Graph partitioning is a mandatory step in distributed graph computing systems. Some existing systems use edge partitioning methods to partition static graphs. However, the structure of the real-world graphs changes dynamically, which leads to unnecessary vertex replicas and load imbalance, reducing the performance of graph computation. In this article, we focus on improving the lower partitioning quality caused by the dynamics of the graph structure. We propose an edge repartitioning algorithm via structure-aware group migration (SAGM-ER). We define a special structure edge group (EG) consisting of multiple edges, which can reduce vertex replicas by migrating to other partitions. In repartitioning, we search for EGs in parallel by a method based on a structure-aware priority and then migrate EGs to reduce vertex replicas. Compared to the state of the art, SAGM-ER can reduce more vertex replicas. We implement SAGM-ER on Powergraph, which reduces the redundant replicas by 63.33%, thus reducing executing time and communication costs in graph computation by 33.72% and 37.51%, respectively.

In the era of cloud computing, to achieve convenient location-based service (LBS), consumers such as users, companies, and organizations prefer subcontracting massive geographical data to public clouds after encryption for privacy and security. However, numerous harmful cyber-attacks happen on those public clouds in an unpredicted and hourly manner. To alleviate those concerns, various secure query schemes on the encrypted data have been proposed in the literature. As a fundamental query of LBSs, forward-secure range query has not been well investigated. To address this issue, we propose a lightweight and forward-secure range query (LS-RQ) on geographically encrypted data, which soundly balances between security and efficiency. Promisingly, we design an index mechanism to manage geographical data on the public clouds, while not compromising the privacy of data. Moreover, our LS-RQ schemes provide a convenient approach to range query on geographically encrypted data on-the-fly. We also rigorously prove that LS-RQ is forward-secure. Finally, extensive experimental studies are performed on both real and synthetic datasets. By observation, our LS-RQ schemes are highly efficient in realistic environments. Particularly, on encrypted datasets with about 1000000 geographical data, our solution to secure range query takes strictly less than a second.

Dynamic pricing plays an important role in solving the problems such as traffic load reduction, congestion control, and revenue improvement. Efficient dynamic pricing strategies can increase capacity utilization, total revenue of service providers, and the satisfaction of both passengers and drivers. Many proposed dynamic pricing technologies focus on short-term optimization and face poor scalability in modeling long-term goals for the limitations of solution optimality and prohibitive computation. In this article, a deep reinforcement learning framework is proposed to tackle the dynamic pricing problem for ride-hailing platforms. A soft actor-critic (SAC) algorithm is adopted in the reinforcement learning framework. First, the dynamic pricing problem is translated into a Markov Decision Process (MDP) and is set up in continuous action spaces, which is no need for the discretization of action space. Then, a new reward function is obtained by the order response rate and the KL-divergence between supply distribution and demand distribution. Experiments and case studies demonstrate that the proposed method outperforms the baselines in terms of order response rate and total revenue.

The pathogen of the ongoing coronavirus disease 2019 (COVID-19) pandemic is a newly discovered virus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Testing individuals for SARS-CoV-2 plays a critical role in containing COVID-19. For saving medical personnel and consumables, many countries are implementing group testing against SARS-CoV-2. However, existing group testing methods have the following limitations: (1) The group size is determined without theoretical analysis, and hence is usually not optimal. This adversely impacts the screening efficiency. (2) These methods neglect the fact that mixing samples together usually leads to substantial dilution of the SARS-CoV-2 virus, which seriously impacts the sensitivity of tests. In this paper, we aim to screen individuals infected with COVID-19 with as few tests as possible, under the premise that the sensitivity of tests is high enough. We propose an eXpectation Maximization based Adaptive Group Testing (XMAGT) method. The basic idea is to adaptively adjust its testing strategy between a group testing strategy and an individual testing strategy such that the expected number of samples identified by a single test is larger. During the screening process, the XMAGT method can estimate the ratio of positive samples. With this ratio, the XMAGT method can determine a group size under which the group testing strategy can achieve a maximal expected number of negative samples and the sensitivity of tests is higher than a user-specified threshold. Experimental results show that the XMAGT method outperforms existing methods in terms of both efficiency and sensitivity.

Many big data applications produce a massive amount of high-dimensional, real-time, and evolving streaming data. Clustering such data streams with both effectiveness and efficiency are critical for these applications. Although there are well-known data stream clustering algorithms that are based on the popular online-offline framework, these algorithms still face some major challenges. Several critical questions are still not answer satisfactorily: How to perform dimensionality reduction effectively and efficiently in the online dynamic environment? How to enable the clustering algorithm to achieve complete real-time online processing? How to make algorithm parameters learn in a self-supervised or self-adaptive manner to cope with high-speed evolving streams? In this paper, we focus on tackling these challenges by proposing a fully online data stream clustering algorithm (called ESA-Stream) that can learn parameters online dynamically in a self-adaptive manner, speedup dimensionality reduction, and cluster data streams effectively and efficiently in an online and dynamic environment. Experiments on a wide range of synthetic and real-world data streams show that ESA-Stream outperforms state-of-the-art baselines considerably in both effectiveness and efficiency.

Facility Relocation (FR), which is an effort to reallocate the placement of facilities to adapt to the changes of urban planning and population distribution, has remarkable impact on many application areas. Existing solutions to the FR problem either focus on relocating one facility (ie 1-FR) or fail to guarantee the result quality on relocating k>1 facilities (ie k-FR). As k-FR problem is NP-hard and is not submodular or non-decreasing, traditional hill-climb approximate algorithm cannot be directly applied. In light of that, we propose to transform k-FR into another facility placement problem, which is submodular and non-decreasing. We theoretically prove that the optimal solution of both problems are equivalent. Accordingly, we are able to present the first approximate solution towards the k-FR, namely FR2FP. Our extensive comparison over both FR2FP and the state-of-the-art heuristic solution shows that FR2FP, although provides approximation guarantee, cannot necessarily given superior results to the heuristic solution. The comparison motivates and, more importantly, directs us to present an advanced approximate solution, namely FR2FP-ex. Extensive experimental study over both real-world and synthetic datasets have verified that, FR2FP-ex demonstrates the best result quality. In addition, we also exactly unveil the scenarios when the state-of-the-art heuristic would fail to provide satisfied results in practice.

Before the publication of particular datasets, in order to protect the private information while preserving the usability as much as possible, desensitization is required. Automatic identification and evaluation of sensitive attributes are prerequisites for targeted desensitization of datasets, sensitivity can also reflect the effect of desensitization in turn. However, existing desensitization systems all rely on predefined desensitization model with respect to manually given sensitivity levels, which is subjective and unable to be applied end-to-end. Besides, there is no way for the user to tell whether the desensitization is performed enough or superfluous. In this demonstration, we present an interactive system for sensitivity-aware desensitization towards tabular data (SaDes). It automatically evaluates the risks of re-identification for arbitrary columns according to record-linkage attack, and performs desensitization accordingly. The risks of re-identification for the desensitized data can be immediately evaluated such that the user can iteratively execute desensitization in order to achieve a better balance between the usability and privacy. To the best of our knowledge, SaDes is the first system that provides automatic sensitivity evaluation and interactive desensitization in a back-to-back manner.

With ever-increasing data streams from various applications such as smart phones, network monitoring, Internet of Things (IoT), etc., unsupervised clustering of data streams has become an important problem for machine learning and big data analysis. As data streams are data-intensive, temporally ordered, and rapidly evolving, efficiently and effectively online clustering of data streams presents a challenging problem [1] .

Abstract The location selection (LS) problem identifies an optimal site to place a new facility such that its influence on given objects can be maximized. With the proliferation of GPS-enabled mobile devices, LS studies have made progress for moving objects. However, the state-of-the-art LS techniques over moving objects assume the new facility has no competitor, which is too restrictive and unrealistic for real-world business. In this paper we study Competitive Location Selection over Moving objects (CLS-M), which takes into account competition against existing facilities in mobile scenarios. We present a competition-based influence score model to evaluate the influence of a candidate. To solve the problem, we propose an influence pruning algorithm to prune objects who are either influenced by inferior candidates or affected by no candidate. Experimental study over two real-world datasets demonstrates that the proposed algorithm outperforms state-of-the-art LS techniques in terms of efficiency.

Contact tracing is a critical tool in containing epidemics such as COVID-19. Researchers have carried out a lot of work on contact tracing. However, almost all of the existing works assume that their clients and authorities have large storage space and powerful computation capability and clients can implement contact tracing on their own mobile devices such as mobile phones, tablet computers, and wearable computers. With the widespread outbreaks of the epidemics, these approaches are of less robustness to a larger scale of datasets when it comes to resource-constrained clients. To address this limitation, we propose a publicly verifiable contact tracing algorithm in cloud computing (PvCT), which utilizes cloud services to provide storage and computation capability in contact tracing. To guarantee the integrity and accuracy of contact tracing results, PvCT applies a novel set accumulator-based authentication data structure whose computation is outsourced, and the client can check whether returned results are valid. Furthermore, we provide rigorous security proof of our algorithm based on the q -Strong Bilinear Diffie–Hellman assumption. Detailed experimental evaluation is also conducted on three real-world datasets. The results show that our algorithm is feasible within milliseconds of client CPU time and can significantly reduce the storage overhead from the size of datasets to a constant 128 bytes.

Linear discriminant analysis (LDA) is one of the important techniques for dimensionality reduction, machine learning, and pattern recognition. However, in many applications, applying the classical LDA often faces the following problems: (1) sensitivity to outliers, (2) absence of local geometric information, and (3) small sample size or matrix singularity that can result in weak robustness and efficiency. Although several researchers have attempted to address one or more of the problems, little work has been done to address all of them together to produce a more effective and efficient LDA algorithm. This article proposes 3E-LDA, an enhanced LDA algorithm, that deals with all three problems as an attempt to further improve LDA. It proposes to learn a weighted median rather than the mean of the samples to deal with (1), to embed both between-class and within-class local geometric information to deal with (2), and to calculate the projection vectors in the null space of the matrix to deal with (3). Experiments on six benchmark datasets show that these three enhancements enable 3E-LDA to markedly outperform state-of-the-art LDA baselines in both accuracy and efficiency.

Graph partitioning is a mandatory step in large-scale distributed graph processing. When partitioning real-world power-law graphs, the edge partitioning algorithm performs better than the traditional vertex partitioning algorithm, because it can cut a single vertex into multiple replicas to apportion the computation. Many advanced edge partitioning methods are designed for partitioning a static graph from scratch. However, the real-world graph structure changes continuously, which leads to a decrease in partition quality and affects the performance of the graph applications. Some studies are devoted to offline repartitioning or batch incremental partitioning, but how to deal with dynamics in real-time is still worthy of in-depth study. In this article, we discuss the impact of dynamic change on partition and discover that both insertion and deletion will lead to local suboptimal partitioning, which is the reason for the degradation of partition quality. As a solution, a dynamic edge partitioning algorithm is proposed to partition dynamics in real-time. Specifically, we deal with dynamics by a distributed stream and improve partition quality by reassigning some closely connected edges. Experiments show that it is robust to initial partition quality, dynamic scale and type, and distributed scale. Compared with the state-of-the-art dynamic partitioner, it can reduce vertex-cuts by 29.5 percent. Compared with the repartitioning algorithms, it can save the partitioning time by 91.0 percent. Applied on the graph task, it can reduce the increase of communication cost and the increase of the total time of task by 41.5 and 71.4 percent.

The facility relocation (FR) problem, which aims to optimize the placement of facilities to accommodate the changes of users’ locations, has a broad spectrum of applications. Despite the significant progress made by existing solutions to the FR problem, they all assume each user is stationary and represented as a single point. Unfortunately, in reality, objects (e.g., people, animals) are mobile. For example, a car-sharing user picks up a vehicle from a station close to where he or she is currently located. Consequently, these efforts may fail to identify a superior solution to the FR problem. In this article, for the first time, we take into account the movement history of users and introduce a novel FR problem, called motion-fr , to address the preceding limitation. Specifically, we present a framework called frost to address it. frost comprises two exact algorithms: index based and index free . The former is designed to address the scenario when facilities and objects are known a priori , whereas the latter solves the motion-fr problem by jettisoning this assumption. Further, we extend the index-based algorithm to solve the general k - motion-fr problem, which aims to relocate k inferior facilities. We devise an approximate solution due to NP-hardness of the problem. Experimental study over both real-world and synthetic datasets demonstrates the superiority of our framework in comparison to state-of-the-art FR techniques in efficiency and effectiveness.

In this paper we propose and study the problem of k-Collective influential facility placement over moving object. Specifically, given a set of candidate locations, a group of moving objects, each of which is associated with a collection of reference points, as well as a budget k, we aim to mine a group of k locations, the combination of whom can influence the most number of moving objects. We show that this problem is NP-hard and present a basic hill-climb algorithm, namely GreedyP. We prove this method with (1 - 1/e ) approximation ratio. One core challenge is to identify and reduce the overlap of the influence from different selected locations to maximize the marginal benefits. Therefore, the GreedyP approach may be very costly when the number of moving objects is large. In order to address the problem, we also propose another GreedyPS algorithm based on FM-sketch technique, which maps the moving objects to bitmaps such that the marginal benefit can be easily observed through bit-wise operations. Through this way, we are able to save more than a half running time while preserving the result quality. Experiments on real datasets verify the efficiency and effectiveness for both algorithms we propose in this paper.

Due to the explosive increase of data in both the aspects of dimensionality and volume, performing k nearest neighbors search over cloud environments has been progressively receiving more attention among researchers in the field of database cloud computing. However, the key challenge for switching k nearest neighbors search from the local server (i.e., traditional way) to the third-party cloud is, that the database which always contains series of sensitive information has to be kept secret against the cloud. In this work, we present a pair of solutions towards Secure k Nearest Neighbors(SkNN) query in outsourced environments. By skillfully utilizing coarse quantization and the cryptography techniques Advanced Encryption Standard(AES) and Paillier homomorphic encryption, we construct a secure Inverted File(IVF) and compute encrypted approximate distances directly to search for high-dimensional data in the third-party cloud provider, and finally find the better tradeoff between the search quality and security. Empirical study over real datasets and practical environments validate our solutions' feasibility, completeness, and practicality. Compared to the state-of-the-art, the proposed solutions resolve the SkNN of high-dimensional data novelly, have very limited response time and provide high privacy protection on the side of both the User and the cloud provider.

Approximate nearest neighbor (ANN) search in high-dimensional space plays an essential role in many multimedia applications. Recently, product quantization (PQ) based methods for ANN search have attracted enormous attention in the community of computer vision, due to its good balance between accuracy and space requirement. PQ based methods embed a high-dimensional vector into a short binary code (called PQ code), and the squared Euclidean distance is estimated by asymmetric quantizer distance (AQD) with pretty high precision. Thus, ANN search in the original space can be converted to similarity search on AQD using the PQ approach. All existing PQ methods are in-memory solutions, which may not handle massive data if they cannot fit entirely in memory. In this paper, we propose an I/O-efficient PQ based solution for ANN search. We design an index called PQB+-forest to support efficient similarity search on AQD. PQB+-forest first creates a number of partitions of the PQ codes by a coarse quantizer and then builds a B+-tree, called PQB+-tree, for each partition. The search process is greatly expedited by focusing on a few selected partitions that are closest to the query, as well as by the pruning power of PQB+-trees. According to the experiments conducted on two large-scale data sets containing up to 1 billion vectors, our method outperforms its competitors, including the state-of-the-art PQ method and the state-of-the-art LSH methods for ANN search.

The location selection (LS) problem aims to mine the optimal location to place a new facility from a set of candidates such that the benefit or influence on a given set of objects is maximized. State-of-the-art LS techniques assume each object is static and can only be influenced by a single facility. However, in reality, objects (e.g., people, vehicles) are mobile and are influenced by multiple facilities. Consequently, classical LS solutions fail to select locations accurately. In this work, we introduce a generalized LS problem called PRIME-LS which takes mobility and probability factors into consideration to address the aforementioned limitations. To solve the problem, we propose an algorithm called PINOCCHIO, which leverages two pruning rules based on a novel distance measure, and further extend it by incorporating two optimization strategies. Experimental study over two real-world datasets demonstrates superiority of our framework in comparison to state-of-the-art LS techniques.

Differential privacy (DP) is a promising tool for preserving privacy during data publication, as it provides strong theoretical privacy guarantees in face of adversaries with arbitrary background knowledge. Histogram, as the result of a set of count queries, serves as a core statistical tool to report data distributions and is in fact viewed as the fundamental method for many other statistical analysis such as range queries. It is an important form for data publishing. In this paper, we consider the scenario of publishing sensitive histogram data with differential privacy scheme. Existing work in this field has justified that, comparing to directly applying differential privacy techniques (i.e., injecting noise) over the counts in histogram bins, grouping bins before noise injection is more effective (i.e., with higher utility) as it introduces much less error over the sanitized histogram given the same privacy budget. However, state-of-the-art works have not unveiled how the overall utility of a sanitized histogram can be affected by the balance between the privacy budget distributed between grouping and noise injection phases. In this work, we conducted a theoretical study towards how the probability of getting better groups can be improved such that the overall error introduced in sanitized histogram can be further reduced, which directly leads to a higher utility of the sanitized histogram. In particular, we show that the probability of achieving better grouping can be affected by two factors, namely privacy budget assigned in grouping and the normalized utility function used for selecting groups. Motivated by that, we propose a new DP histogram publishing scheme, namely IHP (Iterative Histogram Partition), in which we carefully assign privacy budget between grouping and injection phases based on our theoretical study. We also theoretically prove that e-differential privacy can be achieved according to our new scheme. Moreover, we also show that, under the same privacy budget, our scheme exhibits less errors in the sanitized histograms comparing with state-of-the-art methods. Finally, empirical study over three real-world datasets also justifies that our scheme achieves the least error among series of state-of-the-art baseline methods.

The location selection (ls) problem, which aims to mine the optimal location from a set of candidates to place a new facility such that a score (i.e., benefit or influence on some given objects) can be maximized, has drawn significant research attention in recent years. State-of-the-art ls techniques assume each object is static and can only be influenced by a single facility. However, in reality, objects (e.g., people, vehicles) are mobile and are influenced by multiple facilities, which prevents classical ls solutions from selecting accurate results. In this paper, we introduce a generalizedls problem called Prime-ls which takes mobility and probability factors into consideration to address the aforementioned limitations. Specifically, given a set of candidate locations, Prime-ls aims to mine the optimal location which can influence the most number of moving objects. Also, to address the problem we propose an efficient algorithm called Pinocchio that leverages two pruning rules based on a novel distance measure. These rules enable us to prune many inferior candidate locations prior to influence computation, paving the way to efficient and accurate solution. Furthermore, we extend Pinocchio (Pinocchio-vo) by incorporating two optimization strategies during candidate validation phase, which further reduce unnecessary computations. Experimental study over two real-world datasets demonstrates superiority of our framework in comparison to state-of-the-art ls techniques.

State-of-the-art classical influence maximization (IM) techniques are "competition-unaware" as they assume that a group (company) finds seeds (users) in a network independent of other groups who are also simultaneously interested in finding such seeds in the same network. However, in reality several groups often compete for the same market (e.g., Samsung, HTC, and Apple for the smart phone market) and hence may attempt to select seeds in the same network. This has led to increasing body of research in devising IM techniques for competitive networks. Despite the considerable progress made by these efforts toward finding seeds in a more realistic settings, unfortunately, they still make several unrealistic assumptions (e.g., a new company being aware of a rival's strategy, alternate seed selection, etc.) making their deployment impractical in real-world networks. In this paper, we propose a novel framework based on game theory to provide a more realistic solution to the IM problem in competitive networks by jettisoning these unrealistic assumptions. Specifically, we seek to find the "best" IM strategy (an algorithm or a mixture of algorithms) a group should adopt in the presence of rivals so that it can maximize its influence. As each group adopts some strategy, we model the problem as a game with each group as competitors and the expected influences under the strategies as payoffs. We propose a novel algorithm called GetReal to find each group's best solution by leveraging the competition between different groups. Specifically, it seeks to find whether there exist a Nash Equilibrium (NE) in a game, which guarantees that there exist an "optimal" strategy for each group. Our experimental study on real-world networks demonstrates the superiority of our solution in a more realistic environment.

Approximate Nearest Neighbor (ANN) search in high dimensional space has become a fundamental paradigm in many applications. Recently, Locality Sensitive Hashing (LSH) and its variants are acknowledged as the most promising solutions to ANN search. However, state-of-the-art LSH approaches suffer from a drawback: accesses to candidate objects require a large number of random I/O operations. In order to guarantee the quality of returned results, sufficient objects should be verified, which would consume enormous I/O cost. To address this issue, we propose a novel method, called SortingKeys-LSH (SK-LSH), which reduces the number of page accesses through locally arranging candidate objects. We firstly define a new measure to evaluate the distance between the compound hash keys of two points. A linear order relationship on the set of compound hash keys is then created, and the corresponding data points can be sorted accordingly. Hence, data points that are close to each other according to the distance measure can be stored locally in an index file. During the ANN search, only a limited number of disk pages among few index files are necessary to be accessed for sufficient candidate generation and verification, which not only significantly reduces the response time but also improves the accuracy of the returned results. Our exhaustive empirical study over several real-world data sets demonstrates the superior efficiency and accuracy of SK-LSH for the ANN search, compared with state-of-the-art methods, including LSB, C2LSH and CK-Means.

Traditional approaches to video tagging are designed to propagate tags at the same level, such as assigning the tags of training videos (or shots) to the test videos (or shots), such as generating tags for the test video when the training videos are associated with the tags at the video-level or assigning tags to the test shot when given a collection of annotated shots. This paper focuses on automatical shot tagging given a collection of videos with the tags at the video-level. In other words, we aim to assign specific tags from the training videos to the test shot. The paper solves the V2S issue by assigning the test shot with the tags deriving from parts of the tags in a part of training videos. To achieve the goal, the paper first proposes a novel Graph Sparse Group Lasso (shorted for GSGL) model to linearly reconstruct the visual feature of the test shot with the visual features of the training videos, i.e., finding the correlation between the test shot and the training videos. The paper then proposes a new tagging propagation rule to assign the video-level tags to the test shot by the learnt correlations. Moreover, to effectively build the reconstruction model, the proposed GSGL simultaneously takes several constraints into account, such as the inter-group sparsity, the intra-group sparsity, the temporal-spatial prior knowledge in the training videos and the local structure of the test shot. Extensive experiments on public video datasets are conducted, which clearly demonstrate the effectiveness of the proposed method for dealing with the video-to-shot tag propagation.

Hash-based methods achieve fast similarity search by representing high-dimensional data with compact binary codes. However, both generating binary codes and encoding unseen data effectively and efficiently remain very challenging tasks. In this article, we focus on these tasks to implement approximate similarity search by proposing a novel hash based method named sparse hashing (SH for short). To generate interpretable (or semantically meaningful) binary codes, the proposed SH first converts original data into low-dimensional data through a novel nonnegative sparse coding method. SH then converts the low-dimensional data into Hamming space (i.e., binary encoding low-dimensional data) by a new binarization rule. After this, training data are represented by generated binary codes. To efficiently and effectively encode unseen data, SH learns hash functions by taking a-priori knowledge into account, such as implicit group effect of the features in training data, and the correlations between original space and the learned Hamming space. SH is able to perform fast approximate similarity search by efficient bit XOR operations in the memory of a modern PC with short binary code representations. Experimental results show that the proposed SH significantly outperforms state-of-the-art techniques.

Bag-of-features (BOF) derived from local visual features has recently been widely used in content based image classification and scene detection owing to their simplicity and good performance. However, the hyper-dimension of the BOF vector has limited its implementation in large scale datasets because of its high computation complexity. In this paper, we present a new strategy based on the multi-resolution structure of BOF vectors to gain a speed-up of matching. We construct the new structure in two different ways: the uniform quantization method and the non-uniform quantization method. The main idea is to build low level histograms according to the BOF vector. We also introduce the VA-file method in our approach to give an approximation limit in order to accelerate the searching speed of multi-resolution BOF candidate vectors. Experiments results show that our approach has made a great improvement in both efficiency and computational complexity than traditional BOF methods.

As orthogonal moments in the polar coordinate, radial orthogonal moments such as Zernike, pseudo-Zernike and orthogonal Fourier-Mellin moments have been successfully used in the field of pattern recognition. However, the scale and rotation invariant property of these moments has not been studied. In this paper, we present a generic approach based on Jacobi-Fourier moments for scale and rotation invariant analysis of radial orthogonal moments. It provides a fundamental mathematical tool for invariant analysis of the radial orthogonal moments since Jacobi-Fourier moments are the generic expressions of radial orthogonal moments. Experimental results show the efficiency and the robustness to noise of the proposed method for recognition tasks.