To conquer the limitations of traditional watermarking algorithms in adaptively embedding watermarks and to address the constrained feature representation capability of convolution-based methods, this paper introduces a robust deep learning watermarking model named FreqTransNet. This model effectively integrates convolutional modules, Transformer structures, and frequency-domain transformations to enhance its ability in both capturing multi-scale image features and improving robustness. Specifically, the self-attention mechanism inherent in the Transformer effectively models long-range dependencies and global context, thereby improving the model's representation of complex structures. The integration of frequency-domain transformations also mitigates spatial distortions, further amplifying resistance to interference. The extensive experimental results reveal that FreqTransNet surpasses existing state-of-the-art deep learning watermarking approaches in both visual quality and robustness. Compared to the best-performing baseline, FreqTransNet achieves a notable 3.09 dB enhancement in PSNR (from 47.46 dB to 50.55 dB). Additionally, it consistently maintains extraction accuracy exceeding 97% in the presence of various attack scenarios, highlighting its practical applicability and stability in real-world applications.

Blockchain decentralization refers to the characteristics of blockchain systems in which data storage, transaction verification, and ledger state updates are performed and maintained by distributed nodes, rather than relying on the servers of centralized institutions or organizational entities. It is one of the core features of blockchain systems to ensure security, anti-censorship, and system robustness. Quantifying the degree of blockchain decentralization helps identify potential centralization risks and evaluate the fairness of system architectures and governance mechanisms, and provides an important basis for the optimization design for improving consensus efficiency and resource allocation, as well as for evaluating the credibility and understanding the future development trends of blockchain systems in terms of scalability, application domains, and regulatory adaptability. Existing blockchain decentralization measurement methods rely on one or two dimensions, ignore trend analysis of the degree of decentralization, and lack standardized quantitative approaches, such as the consistent application of methods like the coefficient of variation or the information entropy, to enable objective and comparable assessments across different systems. Given that blockchain systems are composed of participating nodes, we adopt a node-centric perspective, and propose a Node-based Blockchain Decentralization Measurement Model (NBDMM). NBDMM uses ten key indicators extracted from three dimensions, node distribution, transaction-network centralization, and node reliability, to measure the degree of decentralization. Experimental results demonstrate that the proposed NBDMM effectively quantifies the level of decentralization across blockchain systems with varying consensus mechanisms. Moreover, findings indicate a trend toward increasing centralization in both Bitcoin and Ethereum, suggesting a gradual erosion of their originally decentralized nature.

Spread spectrum watermarking is a robust methodology for image copyright identification. Embedding location and weight are two critical factors that influence watermark performance. As a rule of thumb, direct current (DC) coefficients are generally excluded from embedding the spread spectrum watermark due to the introduction of block artifacts. We investigate into this issue from the view of watermark embedding and reveal the fact that a desired embedding location should have small module and high stability. The DC location, while exhibiting high stability, is constrained by its large module. To address this limitation, we present a method for adjusting the DC coefficients (ADC). First, the discrete cosine transform (DCT) is performed on a matrix comprised of DC coefficients from all blocks of the host image. Second, the coefficients at the end of the high-frequency band, with the smallest module, are selected to form the embedding location. To adapt different real scenes, an attack-based weight (ABW) method is presented. It incorporates prior attack information into the weight determination. Experimental results demonstrate that the performance of the state-of-the-art algorithms is improved using the adjusted embedding location, and the integration of ADC and ABW methods further enhances the performance of the watermark system, which achieves an average PSNR of 48 dB with the bit error ratio (BER) approaching zero.

Conventional copy-paste augmentations generate new training instances by concatenating existing utterances to increase the amount of data for neural network training. However, the direct application of copy-paste augmentation for anti-spoofing is problematic. This paper refines the copy-paste augmentation for speech anti-spoofing, dubbed CpAug, to generate more training data with rich intra-class diversity. The CpAug employs two policies: concatenation to merge utterances with identical labels, and substitution to replace segments in an anchor utterance. Besides, considering the impacts of speakers and spoofing attack types, we craft four blending strategies for the CpAug. Furthermore, we explore how CpAug complements the Rawboost augmentation method. Experimental results reveal that the proposed CpAug significantly improves the performance of speech anti-spoofing. Particularly, CpAug with substitution policy leads to relative improvements of 43% and 38% on the ASVspoof’ 19LA and 21LA, respectively. Notably, the CpAug and Rawboost synergize effectively, achieving an EER of 2.91% on ASVspoof’ 21LA.

Abstract Blockchain is a single linked list of blocks consisting of transactions identified by their hash value. Querying Blockchain, primarily searching blocks/transactions, can be treated as membership queries and efficiently evaluated by a Bloom filter (BF). The existing BFs for Blockchain, however, do not exploit the data characteristics of Blockchain and the features of modern processors, which otherwise have the potential to greatly improve their performance. This article proposes a novel Bloom filter variant for Blockchain, called Blockchain Bloom Filter (BBF), with three alternatives to improve BF. First, we reorganize BF structure to make use of Single Instruction Multiple Data provided by modern processors, which parallelize the bit‐map and bit‐test processes of BBF and accelerate element insertion and membership querying. On that basis, hash computation of BBF is parallelized, which reduce the hashing time to that of the BF in theory, where k is the number of hash functions. Second, hash functions derived from block/transaction hash are developed to reduce the complexity of hash computation and accelerate the element insertion and membership query processes of BBF. Finally, we further refine BBF structure to limit the mapping range of an element within a cache‐line in the on‐chip cache in order to increase cache efficiency. With the improvements, both theoretical analysis and extensive simulations show that the performance of BBF membership queries greatly exceeds the three state‐of‐the‐art Bloom filter variants, which enables efficient data queries and application specific analysis over Blockchain.

Watermarking involves embedding a watermark in an image and later extracting it to prove the image’s copyright. In most cases, a complete image contains both smooth and textured regions. As a rule of thumb, the visual quality of an image with a watermark embedded in its textured regions is better than that of the same image with a watermark in smooth regions. This paper, by taking advantage of the fact, proposes a texture-aware local adaptive watermarking algorithm to maximize the watermark’s robustness while maintaining its imperceptibility. To identify textured regions in an image, we introduce the texture value, an efficient and proper metric of the richness of image texture. It combines the texture correlation of the AC coefficients, the luminance masking of the DC coefficient, and the distribution of image texture. A watermark is embedded adaptively into multiple non-overlapping textured regions of an image under the specified SSIM condition. Its adaptiveness comes from a novel texture-aware adaptive parameter model derived by multivariate regression analysis. Correct extraction of watermarks from multiple textured regions can be done by the cooperation of embedding and extraction strategies, with the assistance of RS-based watermark coding model. They allow for greater robustness, faster extraction, and adjustable watermark capacity. The simulation experiments on 100 images demonstrate that our proposed algorithm outperforms state-of-the-art algorithms with respect to imperceptibility, robustness, and adaptability.

The continuous k-Nearest Neighbor queries over spatial-textual data streams (abbr. CkQST) retrieve and continuously monitor at most k nearest neighbor (abbr. kNN) objects to the user-specified location containing all the user-specified keywords, which is the core operation of numerous location-based publish/subscribe systems. Such a system is usually subscribed with a massive number of CkQST and evaluated simultaneously whenever new objects are incoming and old objects are expiring. The approach to evaluating CkQST is to construct a spatial-textual hybrid index for subscribed queries and matching the incoming objects utilizing the filtering capabilities of the index. For CkQST, the minimal spatial search range covering kNN objects changes frequently with the arrival and expiration of qualified objects, and the cost of updating the index is prohibitively high. To efficiently evaluate CkQST, we extend Quad-tree with an inverted index, and exploit it with three techniques, i.e. a memory-based cost model, a block-based ordered inverted index and an adaptive insertion strategy. The experiments on comprehensive datasets demonstrate the effectiveness and efficiency of our proposed techniques.

We present the design of and a demonstration for SciDetector, a system of scientific research for online analysis. SciDetector can efficiently online analyze scientific events on large-scale newly arriving data. In modern scientific research, especially astronomy survey, with the development of the observation infrastructure, Short-Timescale and Large Field-of-view (STLF) sky survey has been the major topic. However, existing scientific system focus on offline analysis of long-term historical data, not real-time analysis of large-scale scientific data. Using real data, we demonstrate how SciDetector processes the data and generate the scientific product.

Mobile users prefer to choose personalized travel routes using their mobile terminals. Keyword-aware Optimal Route Query (KORQ) is proposed to meet users' need because it not only considering the length of the route, but also considering the cost of the route and the points of interest covered by the route. As the number of points of interest in road networks increases sharply, the time and space complexity for preprocessing and route expansion rises dramatically, and the state of the art algorithms are not scalable. To address these issues, we propose an algorithm called KORAL, short for Keyword-aware Optimal Route Query Algorithm on Large-scale Road Networks. To reduce the overhead of preprocessing, KORAL partitions the road network into subgraphs, and stores only the information about the routes between subgraphs in preprocessing stages. In the rout expansion stage, KORAL uses a strategy called minimum budget pruning to prune infeasible vertices, which greatly speed up the route expansion. Experiments against 3 datasets of New York road network in a server with 16G RAM show that KORAL is scalable and breaks the limitation that the road network cannot exceed 100K vertices.

Watermarking plays an important role in identifying the copyright of an image and related issues. The state-of-the-art watermark embedding schemes, spread spectrum and quantization, suffer from host signal interference (HSI) and scaling attacks, respectively. Both of them use a fixed embedding parameter, which is difficult to take both robustness and imperceptibility into account for all images. This paper solves the problems by proposing two novel blind watermarking schemes: a spread spectrum scheme with adaptive embedding strength (SSAES) and a differential quantization scheme with adaptive quantization threshold (DQAQT). Their adaptiveness comes from the proposed adaptive embedding strategy (AEP), which maximizes the embedding strength or quantization threshold by guaranteeing the peak signal-to-noise ratio (PSNR) of the host image after embedding the watermark, and strikes the balance between robustness and imperceptibility. SSAES is HSI free by factoring in the priori knowledge about HSI. In DQAQT, an effective quantization mode is proposed to resist scaling attacks by utilizing the difference between two selected DCT coefficients with high stability. Both SSAES and DQAQT can be easily applied to other watermarking frameworks. We introduce a notion called error threshold to theoretically analyze the performance of our proposed methods in details. The experimental results consistently demonstrate that SSAES and DQAQT outperform the state-of-the-art methods in terms of imperceptibility, robustness, computational cost, and adaptability.

It is challenging to retrieve audio clips from large audio datasets not only due to the high dimensionality of audio but also due to the large number of audios. Fingerprinting methods primarily focus on the use of semantic-level techniques to speed up retrieval and neglect low-level support. This paper shows that the performance of audio retrieval can be exploited by properly organizing and manipulating audio fingerprint data. The proposed sampling and counting method markedly improves the retrieval speed while maintaining a high recall rate and high precision for short audio clips. The proposed inverted index structure for fingerprints quickly shrinks the scope of the candidate set while requiring considerably less memory. The experiments show that the proposed method is faster and yields more consistent performance in terms of recall rate and precision than do the state-of-the-art methods. The proposed method is scalable for big audio data and is robust to background noise, resampling, MP3 conversion, white noise addition, bandpass filtering, chorus, echo, flanger, gsm compression, and tremolo. The average precision and recall rates are 99.995% and 99.78%, respectively, for dataset 1 and average 98.97% and 95.15%, respectively, for dataset 2. Specifically, the precision and recall rates for query audio clips of 3 s in length in the targeted dataset 1 are 99.94% and 99.64%, respectively.

Fast audio retrieval is crucial for many important applications and yet demanding due to the high dimension nature and increasingly larger volume of audios on the Internet. Although audio fingerprinting can greatly reduce its dimension while keeping audio identifiable, the dimension for audio fingerprints is still too high to scale up for big audio data. The tradeoff between accuracy (measured by precision and recall rate) and efficiency (measured by retrieval time) prevents further reduction in the dimension of fingerprints. This paper shows that a multi-stage filtering strategy can achieve both speedup and high accuracy, with the beginning stages focusing on speedup and the end stage emphasizing accuracy. With this strategy, an efficient cascaded filtering retrieval method is proposed that consists of filtering with Fibonacci hashing, the middle fingerprint, thresholds to quickly select candidate audios, and refining with an accurate and robust fingerprint on the candidate audios. Experiments with 500 000 audios show that the proposed method can achieve a speed gain more than 28 K times that of the Fibonacci hashing retrieval. After applying MP3 conversion, resampling, white noise addition, and background noise addition, the recall rates of the method are all above 99.45%, and the precision is the same as the Philips audio fingerprint, which is close to 100%.

Workload management is the discipline of effectively managing, controlling, and monitoring work flow across computing systems. It is an increasingly important requirement of database management systems (DBMSs) in view of the trends towards server consolidation and more diverse workloads. Workload management is necessary so the DBMS can be business-objective oriented, can provide efficient differentiated service at fine granularity, and can maintain high utilization of resources with low management costs. The authors see that workload management is shifting from offline planning to online adaptation. In this article, the authors discuss the objectives of workload management in autonomic DBMSs and provide a framework for examining how current workload management mechanisms match up with these objectives. They then use the framework to study several mechanisms from both DBMS products and research efforts. They also propose directions for future work in the area of workload management for autonomic DBMSs.