The Internet of Vehicles (IoV) connects vehicles to the internet, delivering smarter and more convenient mobility services to users. Traditional intelligent service models typically employ federated learning (FL) for training. However, as vehicles continuously encounter new data and exhibit heterogeneous computational capabilities, these models often suffer from catastrophic forgetting and performance degradation. To address the above challenges, this paper focuses on training a personalized federated continual learning (PFCL) model that is expected to achieve good performance on all local tasks. The new PFCL scheme, called adaptive personalized federated continual learning (Adp-PFCL), handles catastrophic forgetting and the lack of personalized solutions. In Adp-PFCL, 1) each vehicle client uses the online continual learning (OCL) algorithm to train its local model in the local training stage to mitigate the forgetting of old knowledge; 2) during the local model update phase, each vehicle client locally executes the privacy-preserving federated deep mutual learning (PP-FedDML) strategy to facilitate personalization and improve the performance of the global model on local data. Extensive experimental results demonstrate that the Adp-PFCL scheme achieves at least a 28.36% improvement in average accuracy while reducing runtime by 7.75% compared with current mainstream approaches, validating its high adaptability to dynamic data and computational efficiency.

Feature selection, as one of the essential dimensionality reduction techniques, hasbecome one popular yet challenging area in the field, such as data mining andmachine learning. Unlike feature extraction (such as principle component analysis andnon-negative matrix factorization), preserving the entire information but losing thefeature relevance. Data processing in feature selection will lose information, leading tothe need to develop lightweight, efficient, and practical methods that preserve the datainformation as much as possible while performing dimensional reduction. In this paper,we propose a rankability-based feature selection method. The rankability concept wasproposed in 2019, similar to the entropy concept, and has not been studied widely yet.The proposed method is lightweight in terms of complexity, which requires no iterativeoptimization or auxiliary estimator tools.We experimented with sixteen datasets and compared our results with four otheralgorithms. The results show that our rankability-based feature selection methodoutperforms the fuzzy entropy-based method on five datasets in eight, and the averageaccuracy increased by 0.1482, 0.1078, and 0.1157, respectively. Then, in the varieddimension-reducing experiments, the proposed method shows superiority on fourdatasets out of eight and is competitive with others on two datasets out of eight.

Gaussian processes (GPs) have attracted considerable attention in assisting evolutionary algorithms (EAs) to solve computationally expensive optimization problems (EOPs) because they can directly provide information about the uncertainty of their predictions. However, the computational complexity of GPs grows cubically as the amount of data increases, which severely limits their computational efficiency in high-dimensional expensive multiobjective optimization problems (EMOPs). To address this limitation, we propose a surrogate-assisted evolutionary algorithm (SAEA) that integrates multilayer perceptron (MLP) grouping with sparse GPs, referred to as MLPSGP-SAEA. First, the MLP grouping selects a subspace from the original space by evaluating the impact of each decision variable on the objective functions. Then, for each objective function, a sparse GP model is employed, and the locations of pseudo-input points are optimized to enhance computational efficiency while improving model accuracy. Moreover, an adaptive sparse and diverse (ASD) infill criterion is proposed, based on the characteristics of the sparse GP model predictive distribution, to better balance exploration and exploitation. Finally, extensive experiments are conducted on four benchmark suites and an aerodynamic design optimization problem. The experimental results demonstrate that MLPSGP-SAEA exhibits significant competitive advantages over the state-of-the-art SAEAs.

Despite notable progress in domain adaptation for classification, regression-based domain adaptation remains challenging, particularly in terms of handling complex data structures, ensuring cross-domain generalization, and maintaining the precision and mathematical rigor required to validate model effectiveness. Unlike classification tasks, which are more resilient to variations in feature scaling, regression tasks are notably more sensitive, making their performance more vulnerable in domain adaptation scenarios. In this paper, we propose a generalized regularization technique grounded in the Grassmannian manifold to address the feature alignment problem. This approach leverages the underlying manifold structure of the data while preserving mathematical bounds, thereby enhancing the precision and efficiency of problem-solving. To demonstrate the effectiveness of the proposed algorithm, we apply it to estimate multi-output parameters in two distinct domains: 1) the Arabidopsis thaliana plant dataset, collected from a high-throughput phenotyping platform at Palacký University, and 2) the publicly available dSprites shape recognition with six adaptation tasks. These tasks are critical to advance agricultural research and address generalization challenges in multi-output regression. Accurate predictions provide deeper insights into plant growth and health, thereby supporting more effective crop management strategies. We evaluate the effectiveness of our framework by comparing it with state-of-the-art regression alignment techniques that are independent of the underlying backbone and adaptable to transfer learning tasks. Experimental results show that our framework consistently outperforms existing methods,results description. The source code will be made publicly available upon acceptance at https://github.com/lingping-fuzzy.

The rapid advancement of Industry 5.0 has accelerated the adoption of the Industrial Internet of Things (IIoT). However, challenges such as data privacy breaches, malicious attacks, and the absence of trustworthy mechanisms continue to hinder its secure and efficient operation. To overcome these issues, this paper proposes an enhanced blockchain-based data storage framework and systematically improves the Delegated Proof of Stake (DPoS) consensus mechanism. A four-party evolutionary game model is developed, involving agent nodes, voting nodes, malicious nodes, and supervisory nodes, to comprehensively analyze the dynamic effects of key factors—including bribery intensity, malicious costs, supervision, and reputation mechanisms—on system stability. Furthermore, novel incentive and punishment strategies are introduced to foster node collaboration and suppress malicious behaviors. The simulation results show that the improved DPoS mechanism achieves significant enhancements across multiple performance dimensions. Under high-load conditions, the system increases transaction throughput by approximately 5%, reduces consensus latency, and maintains stable operation even as the network scale expands. In adversarial scenarios, the double-spending attack success rate decreases to about 2.6%, indicating strengthened security resilience. In addition, the convergence of strategy evolution is notably accelerated, enabling the system to reach cooperative and stable states more efficiently. These results demonstrate that the proposed mechanism effectively improves the efficiency, security, and dynamic stability of IIoT data storage systems, providing strong support for reliable operation in complex industrial environments.

The development of complex networks greatly facilitates the operation of modern society, but it also brings increasingly severe cybersecurity challenges. To effectively safeguard complex networks, this paper employs game theory to analyze the attack-defense confrontation from a macro perspective. A two-player attack-defense game model considering the cascading effect is established. To solve the Nash equilibrium of the game model, this study proposes a parallel compact Dandelion Optimizer algorithm (PCDO). The number of population groups of PCDO can be adjusted to support devices with different memory capacities. The comparative experiments demonstrate that PCDO outperforms other heuristic algorithms on CEC2013 test functions. Experimental results also show that PCDO is more effective than other algorithms in solving the mixed-strategy Nash equilibrium. Furthermore, this study analyzes the impact of different attack-defense resource allocations on the payoff of the attacker.

ABSTRACT Aiming to address issues such as non‐uniform rain density and misjudgment caused by noise in image de‐raining, we propose a single‐image de‐raining method based on a generative adversarial network with contextual information aggregation and multi‐scale feature fusion. First, we design a generator composed of encoding, context information aggregation, and decoding stages. Features are extracted using convolution, while expansion convolution effectively aggregates context information. Transposition convolution is then used to restore the image, enhancing the model's ability to perceive image details and achieve accurate image information judgment and content reconstruction. Second, we design a multi‐scale feature fusion discriminator structure to capture different image details using convolution kernels of different scales and connect feature maps from different scales. This improves the model's ability to understand image details and differentiate between authentic and fake images. Finally, we propose a new refinement loss function to reduce grid artifact generation and add Lipschitz constraints to further minimize the imaging gap. In this paper, peak signal‐to‐noise ratio and structural similarity are used as evaluation criteria, and experiments conducted on real and synthesized rain maps demonstrate the superior rain removal performance of the proposed method.

Single object tracking (SOT) in complex scenes presents significant challenges in computer vision . In recent years, transformer has shown its demonstrated efficacy in visual object tracking tasks, due to its capacity to capture the long-range dependencies between image pixels. However, two limitations hinder the performance improvement of transformer-based trackers. Firstly, transformer splits and partitions the image into a sequence of patches, which disrupts the internal structural information of the object. Secondly, transformer-based trackers encode the target template and search region together, potentially leading to confusion between the target and background during feature interaction. To address the above issues, we propose a fully transformer-based tracking framework via learning structural prior information, called SPformer. In other words, a self-attention spatial-prior generative network is established for simulating the spatial associations between features. Moreover, the cross-attention structural prior extractors based on Gaussian and arbitrary distributions are developed to seek the semantic interaction features between the object template and the search region, effectively mitigating feature confusion. Extensive experiments on eight prevailing benchmarks demonstrate that SPformer outperforms existing state-of-art (SOAT) trackers. We further analyze the effectiveness of the two proposed prior modules and validate their application in target tracking models.

The explosive growth of Internet of Things (IoT) data demands secure and reliable storage, where traditional centralized solutions often fall short. Blockchain offers decentralization and tamper-resistance, making it a promising foundation for IoT. However, IoT blockchain systems based on Delegated Proof-of-Stake (DPoS) face challenges such as weak node incentives, unfair reward distribution, and low consensus efficiency. This paper proposes a fairness-aware incentive mechanism that accounts for both node capability and effort under information asymmetry. By incorporating fairness preferences into the contract design, the mechanism improves participation and motivates sustained effort. Theoretical analysis and simulation results show that our approach enhances throughput by about 15%, while achieving revenue fairness, incentive compatibility, and stronger consensus performance. The mechanism’s adaptability makes it suitable for diverse IoT application scenarios.

Combining cloud radio access network (C-RAN) and mobile edge computing (MEC) can effectively reduce network service latency and improve network reliability. The locations of edge servers (ESs) in the architecture and the services deployed affect the network quality and the operator’s revenue. However, few studies have focused on ES or service deployment in this architecture, and almost no research has addressed both joint deployments. Furthermore, previous studies rarely consider the collaborative deployment across multiple communities (regions) and the cooperation among ESs simultaneously. To fill this gap, this study first constructs a C-RAN network system model with multilayer MEC involving multicommunities and establishes a multiobjective mixed-integer programming (MOMIP) model aimed at maximizing the profit of service providers and the comprehensive average resource utilization of the ESs. Then, for the established model, this study proposes a three-step heuristic algorithm to solve. The algorithm has been used to determine the number of ESs to be deployed in each community, the deployment locations of ESs at each layer, and the services to be deployed on each ES. Finally, this study conducts a series of simulation experiments. Experimental results show that the proposed algorithm outperforms the baseline algorithms, and the obtained deployment scheme is more reasonable.

The combination of Cloud Radio Access Network (C-RAN) and Mobile Edge Computing (MEC) has been proven to effectively enhance network transmission rates and service capabilities. However, since the ground communication facilities in the architecture are fixed, have limited coverage and are easily damaged by natural disasters, some user requests may not be processed in a timely manner. This poses a significant challenge to the flexibility and post-disaster recovery capabilities of the architecture. To this end, this paper studies how to deploy Unmanned Aerial Vehicles (UAVs) to assist the recovery of post-disaster communication network when some ground facilities in the architecture are destroyed, and how to perform reasonable task scheduling based on user task priorities to achieve rapid and effective rescue. Moreover, this paper mathematically models the problem with the goal of maximizing the success rate of user task execution. Since the established model belongs to the Mixed-integer Nonlinear Programming (MINLP) model, which is non-convex and NP-Hard, this paper designs a priority-based algorithm for joint UAV deployment and task scheduling to obtain high-quality suboptimal solutions. The algorithm employs a two-layer optimization architecture with the characteristics of low memory usage and low time complexity. The experimental results indicate that the proposed algorithm outperforms the baseline algorithms and is more effective in handling rescue tasks under different user scales.

With the development of artificial intelligence, the security problem of wireless sensor networks (WSN) is becoming more and more serious. Existing researches mainly consider the confrontation between attacker and defender. In this paper, the sleeper is introduced in the confrontation process. The sleeper exists independently of attacker and defender. The sleeper will choose sides based on the conditions of confrontation game. In this paper, a three-player game model is established to analyze the confrontation game. Large scale strategies make it difficult to find the Nash equilibrium (NE) of the three-player game. In order to solve this problem, a parallel compact particle swarm optimization based on Pareto distribution (p-PCPSO) is proposed. The p-PCPSO not only reduces the use of memory but also improves the optimization performance. Experiments show that p-PCPSO has better optimization performance than other algorithms on 75% test functions of CEC2013. The effectiveness of using p-PCPSO to solve NE is demonstrated by experiments. How different resources and game values influence the benefit of defender are analyzed by experiments. This three-player game model can be extended to intrusion detection and cyber resilience. This paper provides ideas for network security automation and active defense of real-world.

Fetal examinations are a significant and challenging field of healthcare. Cardiotocography is the most commonly used method for monitoring fetal heart rate and uterine contractions. As a promising alternative to cardiotocography, fetal phonocardiography is beginning to emerge. It is an entirely non-invasive, passive, and low-cost method. However, it is tough to estimate the ideal form of the fetal sound signal in most cases due to the presence of disturbances. The disturbances originate from movements or rotations of the fetal body, making fetal heart sound processing difficult. This study presents an automatic method for segmenting the fetal heart sounds in a phonocardiographic signal that is loaded with different types of disturbances and analyzes which of these disturbances most affect segmentation accuracy. To provide a comprehensive investigation, we propose a hybrid classifier based on Transformer and eXtreme Gradient Boosting, short for XGBoost, to improve segmentation performance by decision-making integration. 2000 segments of data from the Research Resource for Complex Physiologic Signals, PhysioNet repository, and created synthetic data (873 recordings) were used for the experiment. In the S1 label, our proposed method ranks first among all compared algorithms in precision, recall, F1, and accuracy score, tying with Transformer in recall score. It achieves an accuracy increase of 5% and 1.3% compared to XGBoost and Transformer, respectively. Similarly, in the S2 label, there is a precision score increase of 5.8% and 3.7% compared to XGBoost and Transformer, respectively. In general, our proposed method shows effective and promising performance..

This study proposes a novel artificial protozoa optimizer (APO) that is inspired by protozoa in nature. The APO mimics the survival mechanisms of protozoa by simulating their foraging, dormancy, and reproductive behaviors. The APO was mathematically modeled and implemented to perform the optimization processes of metaheuristic algorithms. The performance of the APO was verified via experimental simulations and compared with 32 state-of-the-art algorithms. Wilcoxon signed-rank test was performed for pairwise comparisons of the proposed APO with the state-of-the-art algorithms, and Friedman test was used for multiple comparisons. First, the APO was tested using 12 functions of the 2022 IEEE Congress on Evolutionary Computation benchmark. Considering practicality, the proposed APO was used to solve five popular engineering design problems in a continuous space with constraints. Moreover, the APO was applied to solve a multilevel image segmentation task in a discrete space with constraints. The experiments confirmed that the APO could provide highly competitive results for optimization problems. The source codes of Artificial Protozoa Optimizer are publicly available at https://seyedalimirjalili.com/projects and https://ww2.mathworks.cn/matlabcentral/fileexchange/162656-artificial-protozoa-optimizer.

Resource-constrained devices in open environments face diverse optimization problems, so general-purpose optimization capabilities become important but are currently lacking. Our algorithm framework aims to fill this gap and better understand the issues when implementing general optimization in hardware, especially using field-programmable gate array. Therefore, the challenge is to design an algorithm framework that can handle different optimization problems with fewer hardware resources while maximizing solution performance. Based on the Zynq XC7Z020-2CLG400I device and the compact differential evolution (cDE) algorithm, this article describes the unified software and hardware architecture, the cDE algorithm that incorporates ensemble mutation and crossover strategy as well as uniform mutation operation (cDE-emc-um), providing an efficient and low-resource algorithm framework while maintaining generality. This article also theoretically analyzes the global convergence and low computational complexity of the cDE-emc-um and tests the general optimization capabilities of our algorithm framework through two optimization problems.

With the continuous development of the communication service industry, users pay more attention to the quality of network service. Previous studies on offloading problems, especially in the Cloud Radio Access Network (C-RAN) architecture with Mobile Edge Computing (MEC), are primarily focused on the economic perspective, with little consideration given to user-oriented satisfaction problems. To fill this gap, this article proposes a mathematical model for maximizing user satisfaction in the C-RAN architecture with multi-layer MEC. The problem is divided into two stages for solution. The first stage addresses the optimal connection problem between users and Remote Radio Heads (RRHs). The second stage then schedules user tasks reasonably based on the solution obtained in the first stage. The two-stage problems are all proved to be NP-Hard. Two efficient approximation algorithms, namely User-to-RRH Association Algorithm (URAA) and Maximum Satisfaction Algorithm (MSA), are proposed to solve the problems in different stages. This article proves and analyzes the theoretical performance of the two algorithms. Finally, the performance of the proposed algorithms is verified by simulation experiments. The experimental results demonstrate that the two proposed algorithms can achieve reasonable solutions to the problems, and the user satisfaction level can be maintained at a high level.

Secret sharing is particularly important in the field of information security, which allows for the reconstruction of secret information from secure shares. However, due to the large amount of data and non-integer data type of 3D (three-dimensional) models, it is less efficient to perform sharing compared to the 2D(two-dimensional) digital images. In order to enable efficient sharing of 3D models as well, this article designs different circuit modules to parallelize the process of sharing 3D models. The proposed structure exploits the vertex extension to perform a complete reconstruction of the 3D model. In the sharing phase, the symmetric similarity of 3D model is exploited to realize the sharing in parallel, which improves the efficiency of sharing. In the reconstruction phase, the Strassen algorithm is used to optimize matrix multiplication, which saves circuit resources. Simulation results show that the structure performs more than 100 times faster than software. The utilization of circuit resources and the size of the share are both optimized. The validity of the designed hardware architecture is confirmed after analyzing the data from experiments performed on several 3D models.

An issue of great concern in the maintenance of critical infrastructures is the timely and accurate detection of mechanical faults, before these faults deteriorate into failures, causing major service disruptions. The ubiquity of rolling-element bearings (REBs) provides much incentives for online diagnosis of REB faults, spurring a crescendo of research activities that started decades ago. Near-perfect accuracies of state-of-the-art (SOTA) classifiers leveraging deep learning (DL) on the public-domain Case Western Reserve University (CWRU) dataset have been reported. However, the faults covered by this dataset were artificially induced in a controlled environment to bypass the class imbalance problem, and do not include cage faults. Among the open questions to be addressed are: How reproducible are SOTA performances? What are the associated resource requirements? How transferable is SOTA classification performance from CWRU data to actual field data covering naturally occurring faults? How well do SOTA classifiers perform when trained on this real-world dataset, without and with mitigation of the class imbalance problem? How generalisable is classifier performance in the real-world context? Set out to answer the preceding questions, the experimental study presented here provides (i) benchmark results on the CWRU dataset and a real-world dataset from South Australian Water Corporation (SA Water), and (ii) derived from the benchmark results, new insights regarding the state of the research field as well as an answer to the question in the title of this paper: is the problem of REB fault diagnosis a solved problem?

Meta-heuristic algorithms have been widely used in deep learning. A hybrid algorithm EO-GWO is proposed to train the parameters of long short-term memory (LSTM), which greatly balances the abilities of exploration and exploitation. It utilizes the grey wolf optimizer (GWO) to further search the optimal solutions acquired by equilibrium optimizer (EO) and does not add extra evaluation of objective function. The short-term prediction of traffic flow has the characteristics of high non-linearity and uncertainty and has a strong correlation with time. This paper adopts the structure of LSTM and EO-GWO to implement the prediction, and the hyper parameters of the LSTM are optimized by EO-GWO to transcend the problems of backpropagation. Experiments show that the algorithm has achieved wonderful results in the accuracy and computation time of the three prediction models in the highway intersection.

The construction and governance of smart cities require the collaboration of different systems and different regions. How to realize the monitoring of abnormal hot spots through the collaboration of subsystems with limited resources is related to the stability and efficiency of the city. This work constructs a hot data processing framework for drones and 5G edge computing infrastructure, as well as an Ensemble Multi-Objective Cooperative Learning method to process three different types of hot data. The data collection phase combines set operations with the 0-1 multi-knapsack model, and the cooperative learning phase realizes the degree of cooperation control while retaining the ability of independent optimization of the subsystem. Finally, the advantages of the framework are verified by hot data coverage and collaborative processing efficiency, resource use cost, and balance.

Coverage is an important indicator to measure the monitoring quality of Wireless Sensor Network (WSN).As a NP-hard problem, it is a mainstream method to introduce swarm intelligence algorithm to solve it.After analyzing the traditional Firefly Algorithm (FA), aiming at the defects of this algorithm, this paper proposes Modified Communication Parallel Firefly Algorithm (MCPFA) family algorithm, which improves the performance of the algorithm to a certain extent.On this basis, the compact optimization method is introduced and the Modified Communication Parallel Compact Firefly Algorithm (MCPCFA) family algorithm is proposed to further improve the overall function of traditional FA.The proposed algorithm is tested by several classical functions in CEC2013 test function set to verify the performance of the algorithm.Finally, a WSN node deployment scheme based on MCPCFA family algorithm is proposed to improve the network coverage.Through simulation experiments, compared with the traditional Partial Swarm Optimization (PSO), FA, Parallel FA (PFA) and Compact FA (CFA), MCPCFA family algorithm shows the best performance in WSN network layout.

In Cloud Radio Access Networks (C-RANs), the performance of real-time Internet of Things (IoT) content retrieval is improved by placing/requesting the contents into/from remote radio heads (RRHs) and baseband units (BBUs). In the previous studies for the problem in jointly placing user contents and allocating storage allocation of RRHs and BBUs, a strategy of minimizing system resource consumption or transmission delay was used. In this article, we adopt a distinct strategy to maximize the profit of content retrieval services under the constraints of meeting the real-time requirements of users and the limited system resources in C-RANs. The problem is formulated as integer linear programming (ILP). Then, an algorithm for solving the ILP is proposed, and it can provide an approximate solution close to the optimal one with a bounded factor. In the simulation conducted, the results verified the above claims. Further, another algorithm is proposed to effectively expand the storage budgets in C-RANs. By controlling a bounded factor, not only the system performance is guaranteed, but also the upper limit of the expanded storage budget is provided. The smaller the bounding factor, the stricter the performance guarantee, but the expected storage budget will increase.

While it is risky considering spacecraft constraints and unknown environment on asteroid, surface sampling is an important technique for asteroid exploration. One of the sample return missions is to seek an optimal landing site, which may be in hazardous terrain. Since autonomous landing is particularly challenging, it is necessary to simulate the effectiveness of this process and prove the onboard optical hazard avoidance is robust to various uncertainties. This paper aims to generate realistic surface images of asteroids for simulations of asteroid exploration. A SinGAN-based method is proposed, which only needs a single input image for training a pyramid of multi-scale patch generators. Various images with high fidelity can be generated, and manipulations such as shape variation, illumination direction variation, super resolution generation are well achieved. The method's applicability is validated by extensive experimental results and evaluations. At last, the proposed method has been used to help set up a test environment for landing site selection simulation.

The wide application of wireless sensor networks (WSN) brings challenges to the maintenance of their security, integrity, and confidentiality. As an important active defense technology, intrusion detection plays an effective defense line for WSN. In view of the uniqueness of WSN, it is necessary to balance the tradeoff between reliable data transmission and limited sensor energy, as well as the conflict between the detection effect and the lack of network resources. This paper proposes a lightweight Intelligent Intrusion Detection Model for WSN. Combining k-nearest neighbor algorithm (kNN) and sine cosine algorithm (SCA) can significantly improve the classification accuracy and greatly reduce the false alarm rate, thereby intelligently detecting a variety of attacks including unknown attacks. In order to control the complexity of the model, the compact mechanism is applied to SCA (CSCA) to save the calculation time and space, and the polymorphic mutation (PM) strategy is used to compensate for the loss of optimization accuracy. The proposed PM-CSCA algorithm performs well in the benchmark functions test. In the simulation test based on NSL-KDD and UNSW-NB15 data sets, the designed intrusion detection algorithm achieved satisfactory results. In addition, the model can be deployed in an architecture based on cloud computing and fog computing to further improve the real-time, energy-saving, and efficiency of intrusion detection.

A multiserver environment can improve the efficiency of mobile network services more effectively than a single server in managing the increase in users. Because of the large number of users, the security of users’ personal information and communication information is more important in a multiserver environment. Recently, Wang et al. proposed a multiserver authentication scheme based on biometrics and proved the security of their scheme. However, we first demonstrate that their scheme is insecure against a known session-specific temporary information attacks, user impersonation attacks, and server impersonation attacks. To solve the security weakness, we propose an improved scheme based on Wang et al.’s scheme. The security of our improved scheme is also validated based on the formal security analysis, Burrows–Abadi–Needham (BAN) logic, ProVerif, and informal security analysis. Security and performance comparisons prove the security and efficiency of our scheme.

The key to the problem of node coverage in wireless sensor networks (WSN) is to deploy a limited number of sensors to achieve maximum coverage. This paper studies the hybrid strategies of multiple evolutionary algorithms, and applies them to the problem of WSN node coverage. We first proposed the hybrid algorithm SFLA‐WOA (SWOA) based on Shuffled Frog Leaping Algorithm (SFLA) and Whale Optimization Algorithm (WOA). The SWOA algorithm combines the advantages of SFLA and WOA; that is, it retains the unique evolution model of WOA and also has the excellent co‐evolution capability of SFLA. Secondly, using the mutation, crossover and selection operations of the differential evolution (DE) algorithm to further optimize this hybrid algorithm, the SWOA‐based SFLA‐WOA‐DE (SWOAD) algorithm is proposed. In addition, the performance of SWOA and SWOAD has been tested by 30 benchmark functions in the CEC 2017 test set. Experimental results show that the optimization effects of these two algorithms are very outstanding. Finally, the simulation results show that the optimization algorithm proposed in this paper has a good effect on improving the signal coverage of WSN under the actual three‐dimensional terrain.

Wireless sensor networks (WSN) have gradually integrated into the concept of the Internet of Things (IoT) and become one of the key technologies. This paper studies the optimization algorithm in the field of artificial intelligence (AI) and effectively solves the problem of node location in WSN. Specifically, we propose a hybrid algorithm WOA-QT based on the whale optimization (WOA) and the quasi-affine transformation evolutionary (QUATRE) algorithm. It skillfully combines the strengths of the two algorithms, not only retaining the WOA’s distinctive framework advantages but also having QUATRE’s excellent coevolution ability. In order to further save optimization time, an auxiliary strategy for dynamically shrinking the search space (DSS) is introduced in the algorithm. To ensure the fairness of the evaluation, this paper selects 30 different types of benchmark functions and conducts experiments from multiple angles. The experiment results demonstrate that the optimization quality and efficiency of WOA-QT are very prominent. We use the proposed algorithm to optimize the weighted centroid location (WCL) algorithm based on received signal strength indication (RSSI) and obtain satisfactory positioning accuracy. This reflects the high value of the algorithm in practical applications.

Differential Evolution (DE) is a very simple but powerful population-based stochastic algorithm for complex optimization, and the evolution of a certain individual in DE actually implements the search of the solution space. Usually, the movement of a certain individual from a former location to a new one during evolution is conducted by crossover operation, and there are two commonly used crossover schemes in different DE variants implementing such movement: one is exponential crossover and the other is binomial crossover. Though the binomial crossover conquers the representational bias existing in exponential crossover by separating all the parameters and treating them independently, the binomial crossover still has a bias, called the exploration bias, from a higher dimensional perspective of view. Therefore, in this paper, we first present the reason why such an exploration bias is still existing in the binomial crossover, and then present a novel QUATRE structure to tackle this bias. The new QUATRE structure is different from the former proposed QUATRE algorithm as some novel standards and adaptation schemes are involved into the new structure. This structure is validated under Congress on Evolutionary Computation (CEC) benchmarks for real-parameter single objective optimization, and the experiment results show its superiority.

Differential Evolution (DE) is a very simple but powerful Evolutionary Algorithm (EA) for real-world applications. There are two aspects affecting the overall optimization performance significantly, one is parameter control scheme and the other is trial vector generation strategy. As all the control parameters are employed in generating trial vectors, from this perspective of view, the trial vector generation strategy dominates the overall optimization performance. Therefore, trial vector generation strategies are often designed first, and then the corresponding parameter control schemes are well-tuned thereafter. Both of them constitute the main body of a new DE variant. Here in the paper, a novel DE variant with a new designed trial vector generation strategy as well as novel parameter control scheme is proposed to tackle freight trains scheduling problem. By incorporating both a time-stamp mechanism of the external archive and a novel parameter control scheme, the new algorithm can secure better optimization performance not only on man-made benchmarks but also on real-world applications. Both the man-made benchmarks from Congress on Evolutionary Computation (CEC) test suite and the real-world freight train scheduling problem are employed in the validation of the new DE variant. The experiment results show that it is competitive with other state-of-the-art DE techniques for these optimization problems.

Systolic finite field multiplier over GF(2m), because of its superior features such as high throughput and regularity, is highly desirable for many demanding cryptosystems. On the other side, however, obtaining high-performance systolic multiplier with relatively low hardware cost is still a challenging task due to the fact that the systolic structure usually involves large area complexity. Based on this consideration, in this paper, we propose to carry out two novel coherent interdependent efforts. First, a new digit-serial multiplication algorithm based on polynomial basis over binary field (GF(2m)) is proposed. Novel Toeplitz matrix-vector product (TMVP)-based decomposition strategy is employed to derive an efficient subquadratic space complexity. Second, The proposed algorithm is then innovatively mapped into a low-complexity systolic multiplier, which involves less area-time complexities than the existing ones. A series of resource optimization techniques also has been applied on the multiplier which optimizes further the proposed design (it is the first report on digit-serial systolic multiplier based on TMVP approach covering all irreducible polynomials, to the best of our knowledge). The following complexity analysis and comparison confirm the efficiency of the proposed multiplier, that is, it has lower area-delay product (ADP) than the existing ones. The extension of the proposed multiplier for bit-parallel implementation is also considered in this paper.

Frequent itemset mining (FIM) is one of the most common data mining techniques, which is based on the analysis of the occurrence frequencies of items in transactions. However, it is inapplicable in real-life situations since customers may purchase several units of the same item and all items may no t have the same unit profits. High-utility itemset mining (HUIM) was designed to consider both the quantities and unit profits of items in databases, and has become an emerging and critical research topic in recent decades. The SKYMINE approach was proposed to mine the skyline frequent-utility patterns (SFUPs), by considering both the utility and the occurrence frequencies of items. A SFUP is a non-dominated itemset, where the dominance relationship between itemsets is based on the utility and frequency measures. Mining SFUPs using the SKYMINE algorithm and its (UP)-tree structure requires, however, long execution times. In this paper, we propose a more efficient algorithm named skyline frequency-utility (SFU)-Miner to mine the SFUPs, utilizing the utility-list structure. This latter structure is used to efficiently calculate the actual utilities of itemsets without generating candidates, contrarily to the SKYMINE algorithm and its UP-tree structure. Besides, an array called utility-max (umax) is further developed to keep information about the maximal utility for each occurrence frequency, which can be used to greatly reduce the amount of itemsets considered for directly mining the SFUPs. This property can be used to efficiently find the non-dominated itemsets based on the utility and frequency measures. Substantial experiments have been carried out to evaluate the proposed algorithm’s performance. Results have shown that SFU-Miner outperforms the state-of-the-art SKYMINE algorithm for SFUP mining in terms of runtime, memory consumption, number of candidates, and scalability.

Various cryptosystems, such as elliptic curve and pairing‐based cryptosystems, in resource‐constrained security applications rely on finite field multiplication. For applications such as these, a digit‐serial multiplier has the potential features to achieve a trade‐off between space and time complexities. The authors propose an efficient decomposition of the multiplication into four independent sub‐multiplication units to facilitate parallel processing, which is additionally facilitated by the systolic structures of the sub‐multiplication units. The proposed architecture uses a four‐bit scheme to construct a novel processing element, instead of using only one bit as is currently used in similar multipliers. The results of the synthesis show that the proposed digit‐serial dual basis multiplier eliminates up to 96% of the critical path delay.

In this paper, a novel classifier, called superimposed sparse parameter (SSP) classifier is proposed for face recognition. SSP is motivated by two phase test sample sparse representation (TPTSSR) and linear regression classification (LRC), which can be treated as the extended of sparse representation classification (SRC). SRC uses all the train samples to produce the sparse representation vector for classification. The LRC, which can be interpreted as L2-norm sparse representation, uses the distances between the test sample and the class subspaces for classification. TPTSSR is also L2-norm sparse representation and uses two phase to compute the distance for classification. Instead of the distances, the SSP classifier employs the SSPs, which can be expressed as the sum of the linear regression parameters of each class in iterations, is used for face classification. Further, the fast SSP (FSSP) classifier is also suggested to reduce the computation cost. A mass of experiments on Georgia Tech face database, ORL face database, CVL face database, AR face database, and CASIA face database are used to evaluate the proposed algorithms. The experimental results demonstrate that the proposed methods achieve better recognition rate than the LRC, SRC, collaborative representation-based classification, regularized robust coding, relaxed collaborative representation, support vector machine, and TPTSSR for face recognition under various conditions.

A wireless sensor network is a sensing system composed of a few or thousands of sensor nodes. These nodes, however, are powered by internal batteries, which cannot be recharged or replaced, and have a limited lifespan. Traditional two-tier networks with one sink node are thus vulnerable to communication gaps caused by nodes dying when their battery power is depleted. In such cases, some nodes are disconnected with the sink node because intermediary nodes on the transmission path are dead. Energy load balancing is a technique for extending the lifespan of node batteries, thus preventing communication gaps and extending the network lifespan. However, while energy conservation is important, strategies that make the best use of available energy are also important. To decrease transmission energy cost and prolong network lifespan, a three-tier wireless sensor network is proposed, in which the first level is the sink node and the third-level nodes communicate with the sink node via the service sites on the second level. Moreover, this study aims to minimize the number of service sites to decrease the construction cost. Statistical evaluation criteria are used as benchmarks to compare traditional methods and the proposed method in the simulations.

Privacy preserving in association mining is an important research topic in the database security field. This paper has proposed a blocking-based method to solve the association rule hiding problem for data sharing. It aims at reducing undesirable side effects and increasing desirable side effects, while ensuring to conceal all sensitive rules. The candidate transactions are selected for sanitization based on their relations with border rules. Comparative experiments on real datasets demonstrate that the proposed method can achieve its goals.

Differential Evolution (DE) is arguably a very powerful stochastic real-parameter optimization algorithm, and the performance of DE is highly dependent on parameter control and mutation strategy. Many investigations though have been conducted with regards to parameter control, it is still a tedious but important task for users to make parameter selection regarding to specific problems. This paper presents a new parameter-reduced differential evolution approach, which gets rid of the crossover rate parameter Cr by using an automatically generated crossover matrix and gives a self-adaptive tuning scheme of F value. The only one left parameter in the new proposed algorithm is population size, and it is also can be reduced by a restart with population increase approach. The particles' evolution in the new algorithm can be written in an affine-transformation-like form, so it is named after QUsi-Affine TRansformation Evolutionary (QUATRE) algorithm. The users who use this algorithm do not bother parameter tuning problems and conducted experiments under CEC2013 test suites on real-parameter optimization and COmparing Continuous Optimizers (COCO) framework under Black-Box Optimization Benchmarking (BBOB) show that QUA-TRE algorithm outperforms other state-of-the-art swarm based algorithms including PSO variants, DE variants even some CMA-ES variants.

In this paper, we proposed a Competitive QUasi-Affine TRansformation Evolutionary (C-QUATRE) algorithm. This algorithm is an advancement of a preciously proposed QUATRE algorithm. The QUATRE algorithm is arguably a very powerful stochastic optimization algorithm, and it will appear in CEC2016 conference proceedings with the paper title “QUasi-Affine TRansformation Evolutionary (QUATRE) Algorithm: A Parameter-reduced Differential Evolution Algorithm for Optimization Problem”. It conquers some weaknesses of Differential Evolution (DE) algorithm and it has excellent performance even on multi-modal optimization problem. Here in the paper, we advance a C-QUATRE algorithm which uses a pairwise competition mechanism to enhance the performance of the former proposed QUATRE algorithm. The C-QUATRE algorithm is verified both on CEC2013 test suite for real-parameter optimization and BBOB2009 framework for black-box optimization, and experiment results show that the pairwise competition mechanism is very useful for the enhancement of the QUATRE performance over all these benchmarks.

When data mining techniques are applied to discover useful knowledge behind a large data collection, they are often required to preserve some confidential information, such as sensitive frequent itemsets, rules and so on. A feasible way to ensure the confidentiality is to sanitize the database and conceal sensitive information. However, the sanitization process often produces side effects, thus minimizing these side effects is an important task. An important but ignored fact is that a tradeoff exists within different side effects. When attempting to improve the performance on one dimension, the performance on other dimensions often will be degraded. In this paper, we focus on privacy preserving in association rule mining. Since there is a tradeoff within different side effects, we tried to minimize them from the view of multi-objective optimization. A rule hiding approach based on evolutionary multi-objective optimization (EMO) is proposed. It hides sensitive rules through removing identified items. The side effects on missing non-sensitive rules, ghost rules and data loss are formulated as optimization objectives. EMO is utilized to find a suitable subset of transactions for modification so that side effects can be minimized. Experimental results on real datasets illustrate that the proposed approach can achieve satisfactory results with fewer side effects. In addition, the EMO-based approach can produce multiple hiding solutions in a single run. It provides the opportunity for a user to choose freely the preferred one by preference or experience.

Recently, Lee and Tsai proposed a new authentication method for greyscale document images using the portable network graphics format image; however, their method cannot resist the self‐substitution attack, the same‐position‐substitution attack, or the cut‐off attack. Furthermore, those attacks can be completed by the popular image editing software Adobe Photoshop. Therefore, the authors proposed a security‐enhanced authentication scheme based on Lee and Tsai's method. The authors’ proposed scheme uses three random binary sequences to randomise the binary version of a given greyscale document image, and thus overcomes the security flaws mentioned above. The authors’ proposed scheme is capable of repairing the content of the given stego‐image if attacked by the methods mentioned above. Moreover, the authors’ proposed scheme retains all of the strengths of Lee and Tsai's scheme. The authors improve the opacity of the alpha channel of stego‐image by using Wang and Su's extended secret sharing, and enhance the data security by using Hash functions. The experimental results show that the authors’ proposed scheme is effective for application.

Toeplitz matrix-vector product (TMVP) approach is a special case of Karatsuba algorithm to design subquadratic multiplier in GF(2m). In binary extension fields, shifted polynomial basis (SPB) is a variable basis representation, and is widely studied. SPB multiplication using coordinate transformation technique can transform TMVP formulas, however, this approach is only applied for the field constructed by all trinomials or special class of pentanomials. For this reason, we present a new modified SPB multiplication for an arbitrary irreducible pentanomial, and the proposed multiplication scheme has formed a TMVP formula.

In this paper, two improved classifiers based on the nearest feature line (NFL) are proposed for image classification, where the neighborhood feature line segment-I (NFLS-I) classifier uses the neighborhood of prototypes to select the better-fitted feature lines (FLs) and the neighborhood feature line segment-II (NFLS-II) classifier utilizes the neighborhood of the query sample to choose more-likely FLs. With better selection of FLs, these two classifiers can both improve the recognition performance and the computation problem. A large number of experiments on Soil-100 object database, Yale face database, FEI face database, AR face database, and Jochen triesch static hand posture database are performed to evaluate these two proposed classifiers. The experimental results demonstrate that the proposed NFLS-I and NFLS-II classifiers outperform the original NFL and some other improved NFL classifiers for object recognition, hand posture recognition, and face recognition.

Today, people benefit from utilizing data mining technologies, such as association rule mining methods, to find valuable knowledge residing in a large amount of data. However, they also face the risk of exposing sensitive or confidential information, when data is shared among different organizations. Thus, a question arise: how can we prevent that sensitive knowledge is discovered, while ensuring that ordinary non-sensitive knowledge can be mined to the maximum extent possible. In this paper, we address the problem of privacy preserving in association rule mining from the perspective of multi-objective optimization. A new hiding method based evolutionary multi-objective optimization (EMO) is proposed and the side effects generated by the hiding process are formulated as optimization goals. EMO is used to find candidate transactions to modify so that side effects are minimized. Comparative experiments with exact methods on real datasets demonstrated that the proposed method can hide sensitive rules with fewer side effects.

When people utilize data mining techniques to discover useful knowledge behind large database, they also have the requirement to preserve some information so as not to be mined out, such as sensitive frequent item sets, rules, classification tree and the like. A feasible way to address this problem is to sanitize the database to conceal the sensitive information. In this paper, we focus on privacy preserving in association rule mining. In light of the tradeoff between hiding sensitive rules and disclosing non-sensitive ones during hiding process, we tackle this problem from a point view of multi-objective optimization. A novel association rule hiding approach was proposed based on evolutionary multi-objective optimization (EMO) algorithm. It adopted the model of hiding sensitive rules by deleting some items in database. Three side effects, including sensitive rules not hidden, non-sensitive lost rules and spurious rules were formulated as objectives to be minimized. The EMO algorithm was utilized to find a suitable subset of transactions to modify so that the three side effects can be minimized. Experiment results were reported to show the effectiveness of the proposed approach.

Recently in cryptography and security, the multipliers with subquadratic space complexity for trinomials and some specific pentanomials have been proposed. For such kind of multipliers, alternatively, we use double basis multiplication which combines the polynomial basis and the modified polynomial basis to develop a new efficient digit-serial systolic multiplier. The proposed multiplier depends on trinomials and almost equally space pentanomials (AESPs), and utilizes the subquadratic Toeplitz matrix-vector product scheme to derive a low-latency digit-serial systolic architecture. If the selected digit-size is$d$bits, the proposed digit-serial multiplier for both polynomials, i.e., trinomials and AESPs, requires the latency of$2\left\lceil \!{\sqrt {{m \over d}}} \right\rceil\! $, while traditional ones take at least$O\left({\left\lceil {{m \over d}} \right\rceil} \right)$clock cycles. Analytical and application-specific integrated circuit (ASIC) synthesis results indicate that both the area and the time$ \times $area complexities of our proposed architecture are significantly lower than the existing digit-serial systolic multipliers.

As cloud computing and wireless body sensor network technologies become gradually developed, ubiquitous healthcare services prevent accidents instantly and effectively, as well as provides relevant information to reduce related processing time and cost. This study proposes a co-processing intermediary framework integrated cloud and wireless body sensor networks, which is mainly applied to fall detection and 3-D motion reconstruction. In this study, the main focuses includes distributed computing and resource allocation of processing sensing data over the computing architecture, network conditions and performance evaluation. Through this framework, the transmissions and computing time of sensing data are reduced to enhance overall performance for the services of fall events detection and 3-D motion reconstruction.

In this paper, two improved methods based on nearest feature plane (NFP), called as center-based nearest feature plane (CNFP) and line-based nearest feature plane (LNFP), are proposed for recognition. Borrowing the concept from the nearest neighbor plane (NNP) classifier and center-based nearest neighbor (CNN) classifier, the proposed methods choose the valuable representation of the class to reduce the computational complexity of NFP. At the same time, CNFP and LNFP try their best to get the better performance than NFP classifier. A large number of experiments on Yale face database and soil object database are used to evaluate the proposed algorithms. The experimental result demonstrate that the proposed method take lower computational complexity and achieve better recognition rate than the other improved classifiers.

Vision-based Simultaneous Localisation and Mapping (Visual SLAM) is a new hot topic in intelligent robotic applications. A new method for the implementation of a visual SLAM system with monocular vision is proposed in this paper. The general framework of our system is first displayed, and then all the main sub-processes are described step by step. In our design we use the ORB feature to represent each natural landmark with an improved map management and modified covariance extended Kalman filter (MVEKF) to estimate the 6D pose of a free-moving camera. In order to validate and demonstrate the performance of the system, some related experiments are carried out. The experimental results show that our method is feasible, robust and efficient.

An Echo-Aided Bat Algorithm (EABA) based on measurable movement is proposed to improve optimization efficiency in this study. The conception is to employ the echo time to measure the distance from bats and objective. The bats emit an ultrasound to objective to measure the time of a round trip between their position and objective position. The echo time can guide the bats to correct velocity, direction and movement step. And the bats can more accurately measure the position of objective to adjust its step to find the better solution. There are many scenarios with different population sizes and objective functions to verify the performance of the proposed EABA. The experimental numeric result shows that EABA has better ability of search to improve the quality of the best solution than BA. The solution performance is improved by 45% and 30% for the functions of low complexity and high complexity in comparison with the original bat algorithm, respectively.

In this paper, we present a new method for fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and particle swarm optimization (PSO) techniques. First, we fuzzify the historical training data of the main factor and the secondary factor, respectively, to form two-factors second-order fuzzy logical relationships. Then, we group the two-factors second-order fuzzy logical relationships into two-factors second-order fuzzy-trend logical relationship groups. Then, we obtain the optimal weighting vector for each fuzzy-trend logical relationship group by using PSO techniques to perform the forecasting. We also apply the proposed method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index and the NTD/USD exchange rates. The experimental results show that the proposed method gets better forecasting performance than the existing methods.

In this paper, we present a new method for fuzzy rules interpolation for sparse fuzzy rule-based systems based on interval type-2 Gaussian fuzzy sets and genetic algorithms. First, we present a method to deal with the interpolation of fuzzy rules based on interval type-2 Gaussian fuzzy sets. We also prove that the proposed method guarantees to produce normal interval type-2 Gaussian fuzzy sets. Then, we present a method to learn optimal interval type-2 Gaussian fuzzy sets for sparse fuzzy rule-based systems based on genetic algorithms. We also apply the proposed fuzzy rules interpolation method and the proposed learning method to deal with multivariate regression problems and time series prediction problems. The experimental results show that the proposed fuzzy rules interpolation method using the optimally learned interval type-2 Gaussian fuzzy sets gets higher average accuracy rates than the existing methods.

The man-in-the-middle (MITM) attack is the major threat for handheld devices to agree on a session key in which they do not share any prior secret in advance, even if these devices are physically located in the same place. Apart from insecurely typing passwords into handheld devices or comparing long hexadecimal keys displayed on the devices' screens, many other human-verifiable protocols have been proposed in the literature to solve the problem. Unfortunately, most of these schemes are unscalable to more users. Even when there are only three entities attempting to agree on a session key, these protocols need to be rerun three times. In this paper, we present a bipartite and a tripartite authentication protocol using a temporary confidential channel. Besides, we further extend the system into a transitive authentication protocol that allows multiple handheld devices to establish a conference key securely and efficiently. In addition, we provide a formal proof to our protocol to demonstrate our scheme is indeed secure. We also implement the prototype of the system on a mobile phone with satisfying performance.

In this paper, a passive continuous authentication system based on both the hard and the soft biometrics is implemented. The passive continuous authentication system keeps verifying the user without interrupting the user concentrating on his work. It also provides the capacity for the machine to recognize who is in front of the terminal, reduces the potential security leak of the user is temporarily absent front the terminal, and denies the invader to login the system with the stolen account and password. Our system forces to logoff the user when the authentication result identifies the user leaves his seat, but the system won't logoff the user if the user only turns his face to some other directions. The reliability of the system is verified by 7 registered users. According to the experimental results, combining the soft and the hard biometrics in our theoretical framework achieves the goal of continuous authentication efficiently and correctly.

In this study, we propose an ACS with flip-flop search strategy to find the route from source node to destination node in an Ad hoc network topology. A flip-flop search strategy is to alternate the search direction towards either high pheromone area or low pheromone area iteratively in the evolution process. The proposed Flip-Flop search strategy effectively solves the pheromone-excess problem in ACS. The ants are allowed to select reverse path to avoid the ants affected by the high pheromone concentration and disable the ability of discover new search area in routing phase. In simulations, the proposed Flip-Flop Ant Colony System (FFACS) is compared with Traditional Ant Colony System in conditions of various deployment densities and topologies of wireless sensor network. The results show that the FFACS has promising ability of discover new search area to reach the better optimal solution than the TACS has. In addition, the robustness and the stability of FFACS are better than TACS.

A novel robust image watermarking scheme is presented to embed a high capacity watermark into the feature point based characteristic regions. The watermark embedding positions are first determined by the scale-invariant feature transform (SIFT) based local circular regions. Then the binary watermark image is embedded by quantization in the Non-subsampled Contourlet Transform (NSCT) domain. In order to achieve rotation invariance, the watermark is embedded adaptively to the orientation of the region. Simulation results show that the proposed scheme can achieve high invisibility and it can efficiently resist traditional signal processing attacks and geometric attacks.

How to segment moving objects accurately and rapidly is one of the most important problems in computer vision. In this paper, we discuss two methods studied previously for segmenting moving objects based on graph cut. One method employs graph cut to segment objects automatically, and the other uses the graph cut and the C-V model (simplified Mumford-Shah model) to segment objects. As well, we present a fast segmentation algorithm based on graph cut. The proposed algorithm is trying to reduce the number of the nodes in constructing network graph. By mapping the invariable pixels of the difference image into one or several nodes and mapping the variable pixels into other nodes, the number of the nodes and the edges is decreased and the speed of graph cut is increased. In the end of the paper, we compare the characteristics of three methods. Experimental results show that we can get effective and fast results in motion segmentation by using graph cut technology.

In this paper, we present a new weighted fuzzy interpolative reasoning method for sparse fuzzy rule-based systems. The proposed method uses weighted increment transformation and weighted ratio transformation techniques to handle weighted fuzzy interpolative reasoning in sparse fuzzy rule-based systems. It allows each variable that appears in the antecedent parts of fuzzy rules to associate with a weight between zero and one. Moreover, we also propose an algorithm that automatically tunes the optimal weights of the antecedent variables appearing in the antecedent parts of fuzzy rules. We also apply the proposed weighted fuzzy interpolative reasoning method to handle the truck backer-upper control problem. The proposed weighted fuzzy interpolative reasoning method performs better than the ones obtained by the traditional fuzzy inference system (2000), Huang and Shen's method (2008), and Chen and Ko's method (2008). The proposed method provides us with a useful way to deal with weighted fuzzy interpolative reasoning in sparse fuzzy rule-based systems.

This paper proposes a scheme to hide some extra confidential data in transparencies during secret image encryption in visual cryptography. The secret image is multitoned into several levels first. An extended non-expansion visual secret sharing model is employed, i.e. size of transparencies is equal to that of the secret image. Thus less time and space are needed for transparencies transmission and storage.

A reversible watermarking algorithm based on piecewise modification operation(PMO) of triplets is proposed in this paper. PMO is designed to embed a bit into any two pixels while remaining the intensity sum of pixels unchanged. Two bits are embedded into a triplet by repeatedly using PMO on every two neighboring pixels. Another advantage of using PMO is that the capacity consumed by the additional information can be largely decreased. As a result, the embedding capacity is considerably increased. A series of experiments is conducted to verify effectiveness and advantages of the proposed approach.

Automatic image annotation (AIA) is a promising way to improve the performance of image retrieval. In this paper, we propose a novel AIA scheme based on multiple-instance learning (MIL). By introducing the minimum reference set (MRS) into MIL (denoted by MRS-MIL), the positive instances (i.e. regions in images) embedded in the positive bags (i.e. images) can be picked out via reliable inferring for a concept. Generated through the 1-NN classifier, MRS denotes the set of minimum number of bags that correctly classify all the labeled bags. Following the principle of structure risk minimum, MRS shows good generalization ability and is particularly suitable for the problem of being short of labeled training bags, i.e. problem of small samples. Compared with the previous annotation approaches, the experimental results demonstrate that the proposed MRS-MIL based annotation scheme achieves better performance of AIA even with a small set of labeled bags.

A novel reversible data hiding scheme based on invariability of the sum of pixel pairs and pairwise difference adjustment (PDA) is presented in this letter. For each pixel pair, if a certain value is added to one pixel while the same value is subtracted from the other, then the sum of these two pixels will remain unchanged. How to properly select this value is the key issue for the balance between reversibility and distortion. In this letter, half the difference of a pixel pair plus 1-bit watermark has been elaborately selected to satisfy this purpose. In addition, PDA is proposed to significantly reduce the capacity consumed by overhead information. A series of experiments is conducted to verify the effectiveness and advantages of the proposed approach.

A novel reversible data hiding scheme based on an integer transform is presented in this paper. The invertible integer transform exploits the correlations among four pixels in a quad. Data embedding is carried out by expanding the differences between one pixel and each of its three neighboring pixels. However, the high hiding capacity can not be achieved only by difference expansion, so the companding technique is introduced into the embedding process so as to further increase hiding capacity. A series of experiments are conducted to verify the feasibility and effectiveness of the proposed approach.

A novel reversible data hiding scheme is proposed in this article. Each pixel is predicted by its right neighboring pixel in scan order to get its prediction-error. Then, a companding technique is introduced so as to largely increase the number of prediction-errors available for embedding. Accordingly, a location map recording available positions can be compressed into a short bitstream. By largely decreasing the capacity consumed by the compressed location map, the high hiding capacity is achieved. A series of experiments are conducted to verify the feasibility and effectiveness of the proposed approach.

A novel feature extraction method, namely Laplacian discriminant projection with optimized kernels (KLDP-Opt) algorithm is proposed in this paper. The advantage of KLDP-Opt lies in: 1) the similarity matrix is constructed with the class-wise nonparametric similarity measure where it solves procedure selection problem; 2) data-dependent kernel is applied to solve the limitation of linearity of LPP, where the adaptive parameters of the data-dependent kernel are computed through optimizing an objective function designed for measuring the class separability of data in the feature space. Besides the theory derivation, the experiments are implemented on ORL and Yale face databases to evaluate the feasibility of the proposed algorithm.

This paper proposes two transparency authentication schemes used in visual cryptography, which are based on watermarking techniques. In the first scheme, a secret image can be perceptible when stacking two transparencies. In addition, another watermark image is visible when shifting one transparency to an appropriate position and stacking it with the other transparency. In the second scheme, both the secret image and the watermark are of the same size, while in the first scheme, the watermark is half of the size of the secret image. No computer aid is needed in decryption in the first scheme while simple computation is required in the second one. The secret image and the watermark are encrypted at the same time in the two schemes. Experimental results demonstrate the two schemes are effective and practical.

A reversible digital watermarking scheme based on a blockwise difference expansion (DE) method is proposed here. The proposed scheme differs from previous ones in its robustness against cropping and collage attacks. In order to easily detect the correct cropped position for a cropped image, a locating pattern is embedded into the carrier image by a modified patchwork algorithm. Incurred overhead, payload, and authentication information are then collected and embedded into the carrier image by a blockwise DE method. Within the proposed scheme, image ID and block index also serve as parts of the inputs to the hash function. This endows the scheme with capability in resisting collage attacks. The experimental results show that the scheme can achieve a good robustness against cropping attacks as well as collage attacks.

This paper presents a novel face recognition method based on complete Kernel Fisher discriminant (CKFD) analysis of Gabor features with power polynomial models. By integrating the Gabor wavelet representation of face images and the enhanced powerful discriminator named CKFD analysis, the method is robust to changes in illumination and facial expressions and poses. On the other hand, the extended polynomial Kernels, namely fractional power polynomial (FPP) models, are employed in CKFD analysis, which enhance face recognition performance. Comparing with existing PCA, LDA, KPCA, KFD and CKFD methods, the proposed method gives superior results in the ORL and Yale face databases. Its good performance in the two face databases gives the promising idea to solve the pose, illumination, and expression (PIE) problem of face recognition

In this paper, a robust VQ-based digital image watermarking scheme for Fritchman channel model is proposed. Before transmission, a novel VQ-based watermarking scheme with genetic codebook partition is adopted to get higher PSNR values between the original and watermarked images, and then the genetic algorithm is adopted to jointly optimize the channel distortion and the bit-correct-rate of the extracted watermarks. The performance comparison of IA for BSC model and Fritchman model is presented, the simulation results also show that the genetic algorithm based jointly optimized method is effective.

The work presents a novel, efficient, nearest-neighbor codeword search algorithm based on three elimination criteria in the Hadamard transform (HT) domain. Before the search process, all codewords in the codebook are Hadamard-transformed and sorted in the ascending order of their first elements. During the search process, we first perform the HT on the input vector and calculate its variance and norm, and secondly exploit three efficient elimination criteria to find the nearest codeword to the input vector using the up-down search mechanism near the initial best-match codeword. Experimental results demonstrate that the performance of the proposed algorithm is much better than that of most existing nearest neighbor codeword search algorithms, especially in the case of high dimension.

An important method in pattern recognition is k nearest-neighbor classification. However, its computational complexity limits its real-time applications. The partial distance search is a solution to this problem. Although it is not very effective, it can be combined with other algorithms to reduce the complexity. The paper proposes an indexing method that uses the variance vector of feature vectors of a design set to improve the efficiency of the partial distance search. Experimental results indicate the effectiveness of this indexing preprocessing

For a multi-user-based watermarking system, a new scheme for providing the function of secret sharing is proposed. A user-key generating procedure is introduced to generate one master key and several normal keys. By using either of these normal keys, a secret watermark is obtained from the cover image. By referring to the original watermark and all the generated secret watermarks, a public watermark is created and embedded into the cover image. The proposed scheme does not require the original image to be presented during extracting. To reveal the genuine watermark from the watermarked image, except for the super-user who can extract it directly by using the master key, the normal users who share the secret can only achieve it by presenting the shadow watermarks extracted by using their own keys

Vector quantization (VQ)-based data hiding and watermarking techniques form a newly developed branch in the digital watermarking research field. We propose a novel watermarking scheme for image-in-image data hiding based on VQ. Unlike existing schemes in the literature for embedding a binary watermark into the original image with VQ, we present an efficient and secure algorithm for embedding a gray-level watermark into the original. The offered scheme is robust to attacks such as VQ compression and JPEG compression. Moreover, it greatly expands the capacity of the watermarking system. Experimental results demonstrate the superiority of our scheme in watermark robustness and watermark capacity. The results also prove the effectiveness of our proposed schemes in VQ-based image-in-image data hiding.

A digital watermarking scheme based on vector quantisation (VQ) for gray watermark is proposed. It begins with the procedure of encoding the gray watermark, and embeds the encoded indices into the cover image in VQ domain then. To improve the performance of the watermarking scheme, a genetic index assignment (GIA) procedure, which modifies the signal of the watermark to suit the signal of the cover image, is proposed. The proposed gray watermark embedding scheme is easy to implement, requires no original cover image to be presented during extraction, expands the size of the used watermark, and provides better watermarked results. Experimental results will show the novelty and effectiveness of it.

In this paper, a new and fast encoding algorithm for vector quantization is presented. This algorithm makes full use of two characteristics of a vector: the sum and the variance. A vector is separated into two subvectors: one is composed of the first half of vector components and the other consists of the remaining vector components. Three inequalities based on the sums and variances of a vector and its two subvectors components are introduced to reject those codewords that are impossible to be the nearest codeword, thereby saving a great deal of computational time, while introducing no extra distortion compared to the conventional full search algorithm. The simulation results show that the proposed algorithm is faster than the equal-average nearest neighbor search (ENNS), the improved ENNS, the equal-average equal-variance nearest neighbor search (EENNS) and the improved EENNS algorithms. Comparing with the improved EENNS algorithm, the proposed algorithm reduces the computational time and the number of distortion calculations by 2.4% to 6% and 20.5% to 26.8%, respectively. The average improvements of the computational time and the number of distortion calculations are 4% and 24.6% for the codebook sizes of 128 to 1024, respectively.

A fast codeword search algorithm for vector quantization is presented. Three inequalities based on the characteristics of sums and variances of a vector and its two sub-vectors are introduced to reject a larger number of codewords. Simulation results demonstrate the effectiveness of the proposed algorithm.

A variable-rate mean-match correlation vector quantizer, MMCVQ, is presented as an alternative vector quantizer of side-match vector quantizer (SMVQ) for fast image encoding. Before encoding, the mean values of all codewords are computed, then a sorted codebook is obtained according to the ascending order of the mean values of codewords. During the encoding stage, high correlation of the adjacent image blocks and mean values of the current processing vector and codewords are considered to obtain the state-codebook of the current processing vector. The experimental result shows: (1) the encoding time of MMCVQ is almost 7 times faster than that of full-search VQ and almost 42 times faster than that of SMVQ, and (2) compared with SMVQ, the encoding quality will increase more than 1 dB in PSNR, and (3) the bit rate of MMCVQ is smaller than that of SMVQ by 0.03-0.05 b/pixel.

One of the important factors for image coding of vector quantization (VQ) is the response time of encoding. A new inequality is derived for efficient codeword search of vector quantization. This new inequality is applied to VQ image coding. Experimental results confirm the derivation of the new inequality compared with the method of using the previous vector candidate and triangular inequality elimination (TIE) proposed by Huang and Chen (1990).

This paper describes a methodology for semi-automatic grammar induction from unannotated corpora belonging to a restricted domain. The grammar contains both semantic and syntactic structures, which are conducive towards language understanding. Our work aims to ameliorate the reliance of grammar development on expert handcrafting or the availability of annotated corpora. To strive for a reasonable model for real data, as well as portability across domain and languages, we adopt a statistical approach. Our approach is also amenable to the optional injection of prior knowledge to aid grammar induction, and subsequent hand editing for grammar refinement. This constitutes the semi-automatic nature of the approach. Experiments with the ATIS corpus showed positive results in semantic parsing, when compared to an entirely handcrafted grammar.

Codeword Index Assignment (CIA) is a key issue to vector quantization (VQ). A new algorithm called Modified Tabu Search Algorithm (MTSA) is applied to codeword index assignment for noisy channels for the purpose of minimizing the distortion due to bit errors. Simulated annealing (SA) technique and a new parameter are introduced in the Tabu Search Approach (TSA) to improve the performance of the tabu search approach. Experimental tests show the modified tabu search algorithm is superior to the tabu search algorithm by evaluating the performance of channel distortion after the same number of iterations.

This article concerns the problem and solution of judging fuzzy rule bases according to environmental reinforcements. We propose an on-line, incremental credit assignment algorithm, which takes environmental reinforcement as input and assigns credit to individual rules. The proposed approach adopts a simple updating policy based on recency-weighted average, and demands only small amount of memory. We also contribute to the problem of delayed reinforcement. In the case of delayed reinforcement, the state preference function is constructed iteratively during the exploration phase.

ABSTRACT Vector quantization is a popular technique in low bit rate codingof speech signal. The transmission index of the codevector ishighly sensitive to channel noise. The channel distortion canbe reduced by organizing the codevector indices suitably.Several index assignment algorithms are studied comparatively.Among them, the index allocation algorithm proposed by Wuand Barba is the fastest method but the channel distortion is theworst one. The proposed parallel tabu search algorithm reachthe best performance of channel distortion. 1.INTRODUCTION Vector quantization (VQ) [1] is a widely used technique for datacompression. The binary indices of the optimally chosencodevectors are sent to the destination. A vectorXxx x={, , , } 12  k consisting of k samples of informationsource in the k-dimensional Euclidean space R k is sent to thevector quantizer. The k-dimensional vector quantizer with thenumber of codevectors N is defined as follows by using thereproduction alphabet consisting of N codevectors,Ccc c={,,, }

The multi-mutation rates, multi-crossover rates and a scheme of reinitialization are applied to parallel genetic algorithm for assigning the codevector indices for noisy channels for the purpose of minimizing the distortion caused by bit errors.Experimental results based on the memoryless binary symmetric channel for any bit error demonstrate the robustness of this new approach compared with our previous work [1].The property o f m ultiple global optima is also emphasized in this paper.