With the widespread adoption of multi-view data in numerous fields, multi-view unsupervised feature selection (MUFS) has made notable strides in both feature pruning and missing-view completion. Nonetheless, existing MUFS methods typically rely on centralized servers, which cannot meet real-world demands for privacy preservation and distributed learning, and they often suffer from suboptimal solution and weak convergence guarantees. To address these challenges, IMUFFS, an incomplete multi-view unsupervised federated feature selection via cooperative particle swarm optimization (CPSO) and tensor-aligned learning (TAL) is proposed. Specifically, each client executes CPSO-TAL at two stages: (i) an external optimization phase that involves a CPSO, inspired by the co-evolutionary mechanism of hybrid breeding optimization algorithm, performing a global search in the feature space, and (ii) an internal optimization phase that leverages TAL with imputation and CP decomposition, where CP decomposition reduces dimensionality by decomposing the original tensor into a sum of core components, to learn low-dimensional embeddings, while simultaneously updating anchor graphs and view preference weights, thereby harmonizing imputation and representation learning. On the server side, a federated aggregation strategy using adaptive normalized mutual information (NMI) weighting combines the locally optimized feature selection (FS) weights and NMI scores from clients, ensuring privacy while improving the quality of FS and convergence. Extensive experiments on multiple datasets demonstrate that IMUFFS consistently outperforms state-of-the-art methods, yielding more effective and robust FS and enhancing better missing-view completion.

Federated learning (FL) facilitates privacy-preserving collaborative intelligence, making it ideal for mobile edge computing (MEC)-enabled Industrial Internet of Thing (IIoT). However, the autonomy of participants leads to unstable edge associations, hampering FL deployment. Existing studies typically prioritize device incentives but overlook price competition and network congestion at the server level. To tackle these issues, we propose coMpetition sOftening and coNgestion mItigation (MONI), a communication-efficient incentive mechanism for service pricing. Specifically, MONI employs a dynamic multiteam Bertrand game model to capture boundedly rational interactions among edge servers. It leverages capacity constraints to alleviate price competition and mitigate network congestion, while preserving the uniform stability of the game. Furthermore, MONI incorporates a genetic algorithm augmented with a truncated Gaussian distribution to minimize the disconnection of roaming devices. Experiments on synthetic and real-world industrial datasets demonstrate that MONI reduces recruitment costs and network congestion, increases the number of devices by 18.37%, and boosts model performance by up to 6.39% compared to state-of-the-art benchmarks.

Data driven business applications in logistics industry often issue prediction queries over relational databases to retrieve the newly generated transaction data for feature computations. In some cases even using slightly outdated data can result in significant inaccuracies in predictions. In another cases, we also observed the accuracy of model prediction is not sensitive to the data freshness. In the setting of primary-backup databases, one may choose to fetch the freshest data from the primary database to ensure model accuracy. However, this may hurt the performance of read-write transactions on the primary, especially when subjected to a high volume of prediction requests. In this work, we propose a Model-Accuracy Aware Service that facilitates a flexible trade-off between model prediction accuracy and primary database performance. This service implements an automated routing strategy aimed at minimizing the impact on the primary database's performance while meeting the requirements of model accuracy. It achieves this by leveraging the maintained database freshness information and the predictive results feedback from the model to learn the relationship between data discrepancy and prediction discrepancy in primary-backup scenarios. We report the experimental results on a real logistics application and also show its effectiveness on a public dataset.

Throughout index tuning, existing index advisors allocate tuning budget equally across all queries in the workload, even though a considerable portion of queries benefit negligible from index tuning, leading to high costs and inefficiency. This paper introduces a novel learning-based index advisor named GITEE, which increases tuning efficiency and effectiveness by intelligently guiding the exploration of the large search space on candidate index. Our solution consists of three components. First, we utilize execution plan and predicate information to accurately estimate the maximum improvement indexing can bring, which serves as preliminary knowledge for reasonable tuning budget allocation. Second, we filter out queries based on the impact of indexing on the individual queries and their influence on others, thereby reducing the number of candidate indexes. Third, we leverage a Monte Carlo Tree Search-based solution, guided by the knowledge, to accelerate the selection of high-quality index configurations within the valuable search space. Extensive experiments across various benchmarks demonstrate that GITEE achieves superior tuning performance compared to state-of-theart heuristic or learning-based index advisors, while reducing tuning overhead by 1-2 orders of magnitude.

Update-intensive workloads are prevalent in contemporary OLTP and AI/ML scenarios. An update operation typically involves deleting the old version of the target record and then inserting a new version. In this work, we demonstrate that an LSM-tree faces two issues when dealing with update-intensive workloads. Firstly, the deleted old versions are not promptly garbage collected until they merge with their new versions during compaction. This may lead to space waste and write amplification. Secondly, it is common for an update operation to modify only a small fraction of a data record, such as one of a hundred attributes. However, state-of-the-art LSM-trees fail to effectively utilize the incremental storage strategy, which involves storing only the updated fraction rather than the entire new version to enhance efficiency. In this paper, we propose two techniques, active and fast garbage collection, and adaptive incremental updating, to address these issues, respectively. Active and fast garbage collection probes the distribution of invalid data versions in an LSM-tree and performs garbage collection in a more promptly manner. Adaptive incremental updating applies different storage modes to the update operation to achieve balanced write and read amplification ratios as much as possible. Based on the techniques, we introduce SylphDB implemented based on the codebase of RocksDB and optimized for update-intensive workloads. Experimental results demonstrated that, compared to traditional LSM-tree based systems, SylphDB can improve the efficiency of garbage collection by 2× and reduce write amplification by 20%.

Pan-Tilt-Zoom (PTZ) cameras with wide-angle lenses are widely used in surveillance but often require image rectification due to their inherent nonlinear distortions. Current deep learning approaches typically struggle to maintain fine-grained geometric details, resulting in inaccurate rectification. This paper presents a Forward Distortion and Backward Warping Network (FDBW-Net), a novel framework for wide-angle image rectification. It begins by using a forward distortion model to synthesize barrel-distorted images, reducing pixel redundancy and preventing blur. The network employs a pyramid context encoder with attention mechanisms to generate backward warping flows containing geometric details. Then, a multi-scale decoder is used to restore distorted features and output rectified images. FDBW-Net’s performance is validated on diverse datasets: public benchmarks, AirSim-rendered PTZ camera imagery, and real-scene PTZ camera datasets. It demonstrates that FDBW-Net achieves SOTA performance in distortion rectification, boosting the adaptability of PTZ cameras for practical visual applications.

Although federated learning (FL) enables collaborative training across multiple decentralized devices, strategic clients may be reluctant to participate in FL unless sufficient incentives are available. Existing researches predominantly emphasize short-term incentives, while FL model training is typically a long-term process, and malicious clients may exhibit dishonest behavior during local training. Although designing a long-term incentive mechanism is crucial for FL, this task is challenging due to the heterogeneous nature of FL and the limitations of imperfect system monitoring. To this end, this article designs the first sustainable incentive mechanism for FL called RATE, which aims to incentivize clients to continuously contribute more high-quality data. Specifically, by modeling the competition and cooperation relationship between servers and clients as a multiserver multiclient Stackelberg game, we prove the existence and uniqueness of the Stackelberg equilibrium (SE), and derive the unique SE through the cautious calculation of designed algorithms. Since the derived SE may not be optimal, we further utilize reputation to measure the long-term contribution of clients and build a function between their revenues and reputations to ensure the optimal revenue allocation, thereby maximizing the social welfare of RATE. Extensive experiments on both synthetic and real data sets demonstrate the superiority of RATE. Compared to the state-of-the-art baselines, RATE increases the total utility of the servers by up to 20%, reduces the reputation of malicious clients by up to 90%, and improves the average test accuracy overall.

Modern deep neural networks (DNNs) use supervised learning to model the probability that a sample belongs to its label (termed positive learning), achieving human-level performance in image classification tasks. However, when encountering noisy labels, this process can lead the network to fit erroneous information, diminishing its generalization capability. Scene classification in high-resolution remote sensing images (SC-HRSI) holds considerable potential for diverse applications, also has the challenge of noisy labels when collecting large-scale datasets. Negative learning (NL), where DNNs are trained using complementary labels of samples, is noise tolerant, but often struggles to achieve adequate learning performance on practical challenging datasets. In this letter, we develop an enhanced NL (ENL) module utilizes the underlying sample distribution to refine model learning in the presence of mislabeled data, improving robustness against noise. Concurrently, we propose a selective positive learning (SPL) module, which uses dynamic class-wise confidence scores to filter likely-to-be-clean data, guiding the network toward efficient convergence. Finally, the integrated negative and positive learning (INPL) framework combines these two modules into a unified training pipeline, enhancing the model’s generalization ability and accelerating the convergence process. Experimental results on two widely used SC-HRSI datasets demonstrate the effectiveness of the proposed method compared to state-of-the-art noise robust methods.

Distributed databases are widely used in various fields, such as financial services and e-commerce. These businesses generally exhibit characteristics of large-scale and rapid growth. However, these business systems often suffer from deadlocks that prevent them from operating normally for extended periods. Traditional deadlock detection methods face challenges in scalability and efficiency, especially as the number of nodes increases. Therefore, deadlock detection has always been a research area in distributed databases. In this paper, we introduce an efficient deadlock detection algorithm called HAWK, leveraging a H ierarchical A pproach based on W or K load modeling. Our algorithm addresses these issues by constructing a dynamic hierarchical detection tree that adapts to transaction patterns, significantly reducing time complexity and communication overhead. HAWK first models the workload and generates a predicted access graph (PAG), transforming the problem of partitioning detection task in the basic hierarchical detection into partition detection zone (DZ) in the PAG by a graph-cutting algorithm. Then, leveraging the properties of strongly connected components (SCCs) and deadlock cycles, the SCC-cut algorithm naturally partitions the system-wide deadlock detection into multiple non-intersecting detection zones, thereby enhancing detection efficiency. We used the greedy SCC-cut algorithm to perform a more fine-grained partitioning of the complex PAG. Finally, by periodically sampling and updating the hierarchical structure, the algorithm remains responsive to dynamic workload variations, ensuring efficient detection. Our approach outperforms both centralized and distributed methods, offering a more efficient and adaptive solution. Extensive experimental results demonstrate the effectiveness of the HAWK algorithm, showing significant reductions in the duration of the deadlock and improved system throughput.

As the spatial resolution of satellite remote sensing increases, high-resolution remote sensing (HRRS) images provide richer surface structure details, also make noise and artifacts more obvious than before. Therefore, the building change detection (BCD) task is affected by irrelevant features caused by explicit noise and artifacts, resulting in incoherent semantics and rough boundaries of buildings, which leads to fragmented change detection results and blurred boundaries between background and changes. To address this, we propose the Progressive Temporal Difference Network (PTDNet). PTDNet employs an interleaved CNN-Transformer encoder to enhance semantic structural correlation, supplemented by a Global Information Supplement Module (GISM) for semantic alignment. The Progressive Temporal Difference Module (PTDM) then suppresses artifacts and reinforces change semantic coherence through multi-stage temporal difference fusion. Finally, a Change Guidance Module (CGM) with deep semantic-guided attention refines change boundary. During this process, multi-scale features are effectively aggregated layer by layer to produce the final binary prediction map. We conducted comparative experiments with other state-of-the-art (SOTA) methods on three public BCD datasets, namely WHU, LEVIR and SYSU, and the results show that our PTDNet achieves the highest F1-scores of 93.07%, 91.80% and 83.46% respectively. The code for this work is publicly available at https://github.com/Caijiaqi85/PTDNet-CD.

Segmentation of cell nuclei from three-dimensional (3D) volumetric fluorescence microscopy images is crucial for biological and clinical analyses. In recent years, convolutional neural networks have become the reliable 3D medical image segmentation standard. However, convolutional layers are limited by their finite receptive fields and weight-sharing mechanisms. Consequently, they struggle to effectively model long-range dependencies and spatial correlations, which may lead to inadequate nuclei segmentation. Moreover, the diversity in nuclear appearance and density poses additional challenges. This work proposes a lightweight multi-layer deep aggregation network, MLDA-Net, incorporating Wide Receptive Field Attention (WRFA). This module effectively simulates the large receptive field generated by self-attention in the Swin Transformer while requiring fewer model parameters. This design implements an extended global sensory field that enhances the ability to capture a wide range of spatial information. In addition, the multiple cross-attention (MCA) module in MLDA-Net enhances the output features of different resolutions from the encoder while maintaining global effectiveness. The Multi-Path Aggregation Feature Pyramid Network (MAFPN) receives multi-scale outputs from the MCA module, generating a robust hierarchical feature pyramid for the final prediction. MLDA-Net outperforms state-of-the-art networks, including 3DU-Net, nnFormer, UNETR, SwinUNETR, and 3DUXNET, on the 3D volumetric datasets NucMM and MitoEM. It achieves average performance improvements of 4% to 7% in F1 score, MIoU, and PQ metrics, thereby establishing new benchmark results.

As real-time analytics become increasingly important, more organizations are deploying Hybrid Transactional/An-alytical Processing (HTAP) systems. The HTAP systems, based on a primary/backup replication architecture, usually support real-time read-only queries on backup nodes for the data recently generated by OLTP applications on the primary node. This work is based on the observation that real-time analytical applications often require access to only a fraction of the latest modifications from OLTP applications. However, the state-of-the-art parallel log replay approaches treat all replicated transaction logs equally and replay the entire transaction logs with the same priority which does not take consideration into the OLAP query access pattern. This design can result in increased response latency for real-time applications. This paper presents AETS, an Adaptive Epoch-based Two-Stage log replay framework that implements epoch-based log replay and table group transaction commit. Simultaneously, AETS also takes full account of the table access priority in real-time HTAP workload log replay. It aims to make the data required by analytical queries visible more quickly. Furthermore, AETS includes a two-phase parallel log replay algorithm called TPLR, which achieves lower overhead compared to state-of-the-art algorithms through careful design. We also offer an adaptive fine-grained thread resource allocation method that considers changes in table access patterns over time under thread resource constraints. Our experimental results show that AETS significantly reduces visibility delay for real-time queries. And the results also show that AETS achieves significant replay throughput improvement.

The spatial distribution of the population in urban agglomerations is increasingly changing in the process of rapid urbanization. It is crucial to clarify population distribution mechanisms and spatial characteristics in urban agglomerations for sustainable development. Based on the data of 31 major cities in the Triangle of Central China (TCC) from 2012 to 2020, this study used the population center of gravity and geo-detectors methods to explore the spatio-temporal evolution trends and driving factors at a fine-scale: (1) The center of gravity of TCC is located in the middle of TCC and is moving to the southeast. (2) The industrial facilities are spatially agglomerated and mainly concentrated in the provincial capitals. (3) The changes in population density in TCC have shifted from business to work factors, and the influencing factors vary in each sub-city circle. It is suggested to strengthen industrial development in the central cities of TCC to create more job opportunities, attract talented workforce and increase investments.

Payment channel network (PCN) routing is crucial for ensuring a high system throughput and transaction (TX) success ratio. However, designing PCN routing across IoT blockchain shards is not straightforward. First, the sharded architecture isolates information among multiple shards, increasing difficulties and costs associated with channel probing. Second, the dynamic nature of IoT, e.g., frequent changes in PCN topology and channel states, makes traditional mathematical routing approaches inefficient and even invalid. These challenges inevitably result in a high TX failure ratio and low throughput. This paper presents a novel cross-shard PCN routing IoT blockchain framework that optimizes TX scheduling to maximize long-term throughput and success ratio. Specifically, we propose an efficient cross-shard PCN routing protocol that distinctly categorizes channels into intra-shard and inter-shard types, simplifying cross-shard channel probing and reducing costs. Then, to optimize PCN routing policies in dynamic sharding environments, we propose a deep reinforcement learning algorithm, which includes: 1) multi-agent collaborative learning for the view of incomplete information across shards; 2) a two-layer network architecture to reduce computational complexity on resource-constrained IoT devices. Experiment results show that the proposed cross-shard routing improves 48.4 % of the TX success ratio averaged over baselines, which is 1.56 times of average throughput compared with other routing algorithms in cross-shard PCNs.

Modeling users’ short-term dynamic and long-term static interests to enhance Point-of-Interests (POI) recommendation performance has shown lots of advantages. Since users’ check-in records can be viewed as a graph network, methods based on Graph Neural Networks (GNNs) have recently shown promising applicability for POI recommendation. However, existing GNN-based works have the following shortcomings: (1) ignoring the impact of complex higher-order relationships between user-POI dynamics over time; and (2) ignoring the difference in POI importance that cannot effectively capture the imbalances of geographical influence among POIs. To address these challenges, we propose a novel Self-supervised Long-and Short-term model (SLS-REC) for POI recommendation. Specifically, we first design a spatio-temporal Hawkes attention hypergraph neural network to capture the spatial dependence and temporal evolution in users’ short-term dynamic interests. Then we introduce a dynamic propagation mechanism of GNNs to learn the geographic influences underlying geographic imbalances among POIs. In addition, the contrastive learning framework over a fine-grained node dropout strategy is applied to maximize the mutual information of long and short-term interest representations. Finally, we adaptively unify the recommendation and self-supervised task with an attention-based mechanism to optimize the proposed SLS-REC model for POI recommendation. Experiments on real-world datasets show that the proposed model significantly outperforms state-of-the-art methods.

Developing effective platforms for economic energy management is considered a pivotal issue in the field of electric vehicles (EVs). To implement a cost-effective energy management platform (EMP), developers must overcome two major challenges. The first challenge lies in the environmental dynamic nature, such as EV location, energy price fluctuations, storage levels, and parking availability at charging stations. This causes most traditional one-shot optimizations to fail. The second challenge pertains to the lack of regulation in EV energy exchanges. To address these challenges, we propose a cost-aware two-stage EMP based on blockchain and deep reinforcement learning (DRL), namely, TEMP. Specifically, TEMP first develops a sharding-based blockchain energy management framework, which guarantees trust, security, privacy, traceability, and accountability without the need for intermediaries. Then, considering the complex and high-dimensional environment, TEMP devises a two-stage cooperative scheduling scheme by combining ant colony optimization (ACO) with proximal policy optimization (PPO) to enhance learning effectiveness. Evaluations show that TEMP outperforms the two state-of-the-art baselines by 12.3% and 4.4% in terms of long-term profits while reducing costs by 6.7% and 2.8%, respectively. Moreover, energy transaction efficiency can be ensured when the EV number of blockchain networks is gradually increased.

In the realm of high‐dimensional data analysis, numerous fields stand to benefit from its applications, including the biological and medical sectors that are crucial for computer‐aided disease diagnosis and prediction systems. However, the presence of a significant number of redundant or irrelevant features can adversely affect system accuracy and real‐time diagnosis efficiency. To mitigate this issue, this paper proposes two innovative wrapper feature selection (FS) methods that integrate the ant colony optimization (ACO) algorithm and hybrid rice optimization (HRO). HRO is a recently developed metaheuristic that mimics the breeding process of the three‐line hybrid rice, which is yet to be thoroughly explored in the context of solving high‐dimensional FS problems. In the first hybridization, ACO is embedded as an evolutionary operator within HRO and updated alternately with it. In the second form of hybridization, two subpopulations evolve independently, sharing the local search results to assist individual updating. In the initial stage preceding hybridization, a problem‐oriented heuristic factor assignment strategy based on the importance of the knee point feature is introduced to enhance the global search capability of ACO in identifying the smallest and most representative features. The performance of the proposed algorithms is evaluated on fourteen high‐dimensional biomedical datasets and compared with other recently advanced FS methods. Experimental results suggest that the proposed methods are efficient and computationally robust, exhibiting superior performance compared to the other algorithms involved in this study.

Distributed shared buffer (DSB) is a well-known solution to support multi-master database systems. DSB involves controlling access to shared data among different nodes through a distributed shared buffer and lock-based cache coherence protocols. Existing DSB implementations resolve conflicts at the page level and lack the flexibility required by modern cloud database systems. Authors present HyBuffer which mixes rows and pages in the distributed shared buffer. It enables multiple masters to independently modify different rows on the same page, thereby enhancing concurrency and performance. HyBuffer adopts the hybrid design to track the location information of cached data. A centralized node maintains page locations indicating which masters have cached which pages, and the record locations of a cached page are maintained by the master which has cached this page. This design eliminates the need to redistribute metadata when dynamically adding or removing masters. Experimental results demonstrate that HyBuffer achieves better scalability and performance compared to existing approaches.

Existing works on session-based recommendation have shown the advantage in enhancing the prediction ability of recommendation with various deep learning techniques. However, the following challenges need to be addressed: (1) the hierarchy of item transition patterns is overlooked; (2) existing works fail to distinguish various factors of item transition within a single session for disentangling user intents. To cope with the above challenges, we propose a novel session based recommendation model called S elf-supervised D ual H ypergraph learning with I ntent D isentanglement model ( SDHID ). Specifically, we first propose a disentangled capsule hypergraph convolutional channel for ne-grained intent learning to capture the intra-session pattern. Accordingly, we introduce the hypergraph and capsule networks in disentangling to learn the item embedding for different factors, and then the representation of the intra-session pattern is obtained by aggregating item embedding with attention weights. Moreover, we build a novel dual–primal hypergraph convolutional channel by mapping the hypergraph to a dual–primal graph for learning the item transition pattern of inter-session. In addition, the above two channels are combined into a self-supervised contrastive learning framework by maximizing mutual information between the learned session representations. We unify the recommendation and the self-supervised tasks under a primary and auxiliary learning framework. The combined optimization of two tasks leads to a hierarchical joint learning item transition for intra- and inter-session. Extensive experiments on real datasets show that the proposed model outperforms several state-of-the-art models.

Considering that Butterfly Optimization Algorithm (BOA) has low precision, slow convergence speed and is easy to fall into local optimal problems, this paper tries to apply cooperative search strategies for improvement of BOA. Firstly, the Levy flight search strategy is utilized to expand both the global search ability and the local search ability for BOA. Secondly, the differential variation search strategy is introduced in the local search phase, which improves local search ability and helps to jump out of the local optimum. Finally, a dynamic adaptive search strategy is proposed to balance both the global search ability and local search ability, and accelerate the convergence speed of BOA. Simulation results show that, compared with other algorithms, the improved BOA algorithm based on cooperative search strategies accelerates the convergence speed and increases the search accuracy.

Recommender systems have become a crucial intelligent tool, which provides users with personalized services. Graph learning-based recommendation methods treat user-item interactions and the item transitions as pairwise relations but ignore the complex, higher-order interaction information between nodes. Moreover, since most users often interact with few or even no items, graph learning-based recommendation methods suffer from the data sparsity problem, as well as the unbalanced distribution of edges and nodes. To tackle these issues, we propose a Dual Hypergraph-based Self-supervised Learning recommendation model, named DHSL-GM. Specifically, we derive two dual hypergraphs from the user-item bipartite graph, which models the complex high-order user-item interactions by using hypergraph convolution with spectral hypergraph convolution operator. Meanwhile, we design a gated network-based message passing mechanism to dynamically guide message propagation, addressing the problem of the unbalanced distribution of edges and nodes. In addition, to alleviate the data sparsity problem, we design another dual hypergraph convolutional network based on a node discard strategy, which innovatively integrates self-supervised learning into the training of the hypergraph convolutional network. Experimental results on several real datasets demonstrate the superiority and effectiveness of the proposed model.

Image Registration(IR) has been widely applied in biomedical image processing. It is the process of finding an optimal geometric transformation to align two images, which could be defined as a parameter optimization issue. Genetic Algorithm(GA) is one of the most efficient methods for solving complex optimization problems and it has been applied to the real-coding IR problem. However, the classical GA suffers from premature convergence and is easy to fall into local optimum. Inspired by heterosis, which is a common phenomenon in biology, this study proposes an improved GA. By artificially simulating the breeding process of Chinese three-line hybrid rice, known as a successful application of heterosis, the original crossover and mutation mechanisms of GA are improved, and a dynamic diversity controller is designed. This study conducts several multimodal biomedical IR experiments to compare the improved GA with state-of-the-art IR methods, the results show that the proposed method outperforms the others in most scenarios with faster convergence speed and greater robustness.

Abstract With the development of image recognition technology, face, body shape, and other factors have been widely used as identification labels, which provide a lot of convenience for our daily life. However, image recognition has much higher requirements for image conditions than traditional identification methods like a password. Therefore, image enhancement plays an important role in the process of image analysis for images with noise, among which the image of low-light is the top priority of our research. In this paper, a low-light image enhancement method based on the enhanced network module optimized Generative Adversarial Networks(GAN) is proposed. The proposed method first applied the enhancement network to input the image into the generator to generate a similar image in the new space, Then constructed a loss function and minimized it to train the discriminator, which is used to compare the image generated by the generator with the real image. We implemented the proposed method on two image datasets (DPED, LOL), and compared it with both the traditional image enhancement method and the deep learning approach. Experiments showed that our proposed network enhanced images have higher PNSR and SSIM, the overall perception of relatively good quality, demonstrating the effectiveness of the method in the aspect of low illumination image enhancement.

Threshold segmentation has been widely used in recent years due to its simplicity and efficiency. The method of segmenting images by the two-dimensional maximum entropy is a species of the useful technique of threshold segmentation. However, the efficiency and stability of this technique are still not ideal and the traditional search algorithm cannot meet the needs of engineering problems. To mitigate the above problem, swarm intelligent optimization algorithms have been employed in this field for searching the optimal threshold vector. An effective technique of lightning attachment procedure optimization (LAPO) algorithm based on a two-dimensional maximum entropy criterion is offered in this paper, and besides, a chaotic strategy is embedded into LAPO to develop a new algorithm named CLAPO. In order to confirm the benefits of the method proposed in this paper, the other seven kinds of competitive algorithms, such as Ant–lion Optimizer (ALO) and Grasshopper Optimization Algorithm (GOA), are compared. Experiments are conducted on four different kinds of images and the simulation results are presented in several indexes (such as computational time, maximum fitness, average fitness, variance of fitness and other indexes) at different threshold levels for each test image. By scrutinizing the results of the experiment, the superiority of the introduced method is demonstrated, which can meet the needs of image segmentation excellently.

A novel Distributed Hash Table BChord is presented in this paper. The standard Chord routes only in clockwise direction. Through adding extra anti-clockwise fingers in Finger Table, BChord adopts bi-directional routing mechanism based on Chord. Though fingers in BChord almost double to Chord, results from theoretic analysis show that BChord evidently minimizes the Average Path Length and increases efficiency of broadcast message compared with Chord.

Fuzzy logic offers the ability to handle uncertainty and imprecision effectively, and is therefore ideally suited to reasoning about trust in P2P Environment. A new P2P fuzzy trust model is presented in this paper. Because Trust-based P2P Computing network is very similar to the interpersonal relationship network, this Fuzzy Trust Model includes two phases: Recommendation Trust Phase and Direct Trust Phase. Recommendation Trust Phase mainly focuses on Extraction of Trust Link and Calculation of Recommendation Trust Degree based on Fuzzy Decision-making method. Fuzzy trust evaluation result will be obtained according to various Factor Sets and Evaluation Sets based on Fuzzy Decision-making method. Direct Trust Phase mainly focuses on updating of Direct Trust Degree.