Fetal anatomical structure segmentation in ultrasound images is essential for biometric measurement and disease diagnosis. However, current methods focus on a specific plane or a few structures, whereas obstetricians diagnose by considering multiple structures from different planes. In addition, existing methods struggle with segmenting fuzzy regions, which leads to performance degradation. We propose a real-time segmentation method called Class-aware Multi-structure Instance Segmentation (CMIS), designed to segment 19 key structures in 3 fetal brain planes to support brain-disease diagnosis. We extract instance information and generate class-aware attention for each class instead of dense instances to save computing resources and provide more informative details. Then we implement cross-layer and multi-scale fusion to obtain detailed prototypes. Finally, we fuse global attention with local prototypes cropped by boxes to generate masks and randomly perturb the boxes during training to enhance robustness. Moreover, we propose a new fuzzy region-based constraint loss to address the challenge of structures with varying scales and fuzzy boundaries. Extensive experiments on a fetal brain dataset demonstrate that CMIS outperforms 13 competing baselines, with an mDice of 83.41$\pm$0.03% at 37 FPS. CMIS also excels in external experiments on a fetal heart ultrasound dataset, achieving a mDice of 85.73$\pm$0.02% . These results demonstrate the effectiveness of CMIS in segmenting complex anatomical structures in ultrasound and its potential for real-time clinical applications. CMIS is limited to 2D normal standard planes ($\geq$19 weeks). Thus, its generalization to abnormal cases and broader datasets remains to be investigated.

Federated learning (FL) enables collaborative training of a global model while preserving participants' local data privacy, making it ideal for data-sensitive fields like Industrial Internet of Things (IIoT), finance, and healthcare. However, Non-IID data among participants and the presence of malicious participants pose significant challenges to the model's performance and convergence. The global model is difficult to achieve consistent performance across all participants. Therefore, this paper proposes personalized and robust federated learning (PRFL) to handle non-independently and identically distributed (Non-IID) data with malicious participants. First, to enhance the robustness and convergence, a model similarity-based division mechanism is employed. It groups participants with similar data and removes both independent and colluding malicious participants. Second, we propose a three-stage knowledge sharing personalized federated learning framework. Each participant undergoes inner-loop knowledge sharing, outer-loop knowledge sharing, and personalized knowledge distillation, incorporating performance-driven dynamic weighted sharing mechanism. Moreover, extensive experiments demonstrate that PRFL outper forms other advanced personalized federated learning methods across various benchmark datasets, particularly in scenarios with Non-IID data and malicious participants.

Sparse tensors are prevalent in real-world applications, often characterized by their large-scale, high-order, and high dimensional nature. Directly handling raw tensors is impractical due to the significant memory and computational overhead involved. The current mainstream approach involves compressing or decomposing the original tensor. One popular tensor decomposition algorithm is the Tucker decomposition. However, existing state-of-the-art algorithms for large-scale Tucker decomposition typically relax the original optimization problem into multiple convex optimization problems to ensure polynomial convergence. Unfortunately, these algorithms tend to converge slowly. In contrast, tensor decomposition exhibits a simple optimization landscape, making local search algorithms capable of converging to a global (approximate) optimum much faster. In this paper, we propose the FastTuckerPlus algorithm, which decomposes the original optimization problem into two non-convex optimization problems and solves them alternately using the Stochastic Gradient Descent method. Furthermore, we introduce cuFastTuckerPlusTC, a fine-grained parallel algorithm designed for GPU platforms, leveraging the performance of tensor cores. This algorithm minimizes memory access overhead and computational costs, surpassing the state-of-the-art algorithms. Our experimental results demonstrate that the proposed method achieves a 2× to 8× improvement in convergence speed and a 3× to 5× improvement in per-iteration execution speed compared with state-of-the-art algorithms.

Graph-structured data has been widely applied in transportation, molecular, and e-commerce networks, etc. Graph Convolutional Network (GCN) has emerged as an efficient approach to processing non-Euclidean graph data. However, the varying sizes and sparsity of graph datasets, coupled with the dependency of the dataflow patterns in GCN computation on the graph data, have rendered the acceleration of GCN inference increasingly challenging. This paper proposes a GCN inference accelerator based on multi-dataflow and high bandwidth memory (HBM), named DAHBM-GCN. Firstly, we designed a computing engine that supports multiple dataflows, aggregation-first, and combination-first orders. Furthermore, an adaptive selector for the multi-dataflow computing engine based on the decision tree is proposed to select the optimal dataflow computing engine. Secondly, an efficient mapping of pseudo channels (PCs) for multi-channel HBM is devised to enhance bandwidth, effectively alleviating memory latency and bandwidth bottlenecks. Thirdly, a hybrid fixed-point quantization strategy for GCN is introduced, which reduces the GCN model's computation complexity and parameter count with almost no loss of accuracy. Finally, extensive performance evaluation experiments demonstrate that across various datasets, DAHBM-GCN achieved average speedups of 52.5–129.3× and 4.9–7.9× compared to PyG-GCN and DGL-GCN on CPU, respectively. Compared to the AWB-GCN, HyGCN, HLS-GCN, and GCNAX accelerators FPGA-based, DAHBM-GCN also exhibits average speedups of 1.21–2.21×, 1.25–1.98×, 1.65–2.68×, and 1.18–1.56× respectively, on various datasets. Additionally, DAHBM-GCN possesses the advantages of high flexibility and low energy consumption.

The proliferation of vulnerable code poses a significant threat to software system security and user privacy. Given the inefficiency inherent in manual vulnerability analysis, there has been a pronounced surge of interest in automating vulnerability management using machine learning techniques. However, the scarcity of publicly accessible and large-scale datasets in the vulnerability domain impedes the advancement of automated methodologies. The advent of federated learning has introduced the potential utilization of private data for learning, while ensuring privacy and security within this paradigm presents a novel challenge. To solve this problem, we introduce a new approach called vulnerability solution with abstract syntax tree (AST), SOEHash, and clustering (V-ASC). We first obtain the AST of the vulnerability code to obtain the underlying pattern of the vulnerability. To protect data privacy as well as to extract vector features of the, we use the SOEHash algorithm to process the AST. Finally, to speed up the process of similarity comparison between vectors, we use an unsupervised clustering algorithm to transform the set of vectors into individual vulnerability clusters. Experiments on a recent vulnerability code dataset validate the effectiveness and efficiency of V-ASC.

Honeypots, which serve as active defense mechanisms, have historically played pivotal roles in cyberspace offensive and defensive countermeasure scenarios. However, with the advancement of honeypot recognition technologies, their effectiveness in real-world network defense has gradually diminished. In response, moving target defense (MTD) has recently solidified its position as a proactive cybersecurity strategy and a critical research frontier. MTD leverages heterogeneous, redundant deployments of service resources and randomization techniques to disrupt attack methods. However, despite their advantages, MTD systems face challenges related to high resource consumption. To address these limitations, we propose a moving target defense based on stochastic games and honeypots (GH-MTD) framework. This framework consists of four key modules: traffic detection, gaming, MTD, and honeynet. Firstly, malicious traffic is identified through a deep learning-based detection method. Secondly, a zero-sum game model is constructed to capture the decision-making dynamics between defenders and attackers in the context of moving target defense. Subsequently, a cross-scenario adaptive MTD module is designed to route different types of traffic to corresponding virtual server groups. Finally, a honeypot module is implemented to capture and analyze the specific attack behaviors of malicious actors. By integrating honeynet probes with real services and employing attack behavior analysis alongside internet protocol (IP) address redirection techniques, the GH-MTD system achieves a defense response that is both cost efficient and highly effective. Empirical evaluation reveals a 5.5-fold enhancement in attack diversion probability through benchmarking with service-oriented MTD architectures, while the capture rate surpasses that of conventional honeypots by 3.4 times. Particularly against real attackers, GH-MTD exhibits 5.6 times more captured packets and extends the time consumed by attackers by 1.5 times over that of standalone honeypots. In our experiments, we evaluate the architecture's performance against various attack methods, including automated scripts, manual attacks, and assaults by high-level penetration testers. The results demonstrate that the GH-MTD architecture performs exceptionally well, particularly in mitigating and countering advanced, sophisticated attacks, thereby demonstrating its effectiveness in modern network defense strategies.

Both truth discovery and pattern analysis are effective methods for extracting valuable insights from data streams in mobile crowdsensing. However, existing privacy-preserving schemes either suffer from low data utility or provide high utility at the cost of weak privacy protection. To address this challenge, we introduce a robust privacy-preserving scheme that facilitates high-utility truth discovery and pattern analysis over mobile crowdsensing data streams. Concretely, we leverage the Square Wave mechanism, a randomized reporting technique, to perturb the data to prevent privacy breaches. To reduce the utility loss caused by perturbation, we design a budget allocation algorithm. This algorithm ensures that adjacent timestamps with approximate data share a perturbed value derived from their accumulated budgets. Furthermore, to facilitate robust pattern analysis, we propose a data splitting method that divides the perturbed data into two parts: one part records patterns randomly, while the other part recovers the perturbed values. Theoretical analysis confirms that our scheme satisfies ω-event ϵ-differential privacy level. Extensive experiments conducted on four real-world datasets demonstrate that our scheme outperforms existing schemes, delivering more accurate results for both truth discovery and pattern analysis under the same privacy constraints.

Pedestrian historical trajectory completion significantly bolsters the predictive accuracy of models. However, traditional statistical models such as Hidden Markov Models (HMM), which focus solely on individual pedestrian trajectories, often fall short in terms of generalization. Conversely, data-driven deep learning approaches demand extensive and meticulous data annotation as well as large datasets. Additionally, leveraging sequential historical data and uncovering the correlation between neighboring pedestrians during absences presents a significant challenge. To address these issues, we introduce a novel trajectory completion method that harnesses prior-posterior knowledge and interactive information, termed CMPT. Our approach commences with the design of a Neighbor Pedestrian Selection module (NPS), adept at identifying neighboring pedestrians through a composite scoring system that evaluates feature similarity and proximity. Subsequently, we employ a Top-Graph Attention Network (T-GAT) to extract multiple correlation sets between preceding and succeeding moments within the scenario. These correlations are then fed into the Markov-Inverse Recovery module (MR), which utilizes prior and posterior insights to flesh out the neighbor influence at the unobserved intervals. Culminating in the Trajectory Reconstruction module (TR), we integrate the completed neighbor influence data with the historical trajectory of the missing pedestrian to finalize the missing trajectory reconstruction. Empirical evidence from our experiments indicates that the Final Distance Error (FDE) of the trajectories completed by CMPT is a commendable 0.30. The source code for CMPT is available from https://github.com/ZYueliang/CMPT-Net.

Truth discovery endeavors to extract valuable information from multi-source data through weighted aggregation. Some studies have integrated differential privacy techniques into traditional truth discovery algorithms to protect data privacy. However, due to the neglect of outliers and limitations in budget allocation, these schemes still need improvement in the accuracy of discovery results. To solve these challenges, we propose a privacy-preserving scheme called PriPTD to achieve secure and accurate truth discovery services over crowdsourced data streams. Instead of assuming that worker weights are always stable between two neighboring timestamps, we delve deeper to consider outliers where worker weights change rapidly. Accordingly, we develop an outlier-aware weight estimation method with a time series model to capture and handle these outliers. Furthermore, to ensure data utility under a limited budget, we devise a weight-aware budget allocation algorithm. Its core idea is that timestamps with higher importance consume a larger proportion of the remaining budget. Additionally, we design a noise-aware error adjustment approach to mitigate the adverse effects of introduced noise on accuracy. Theoretical analysis and extensive experiments validate our scheme. Final comparative experiments against existing works confirm that our scheme achieves more accurate truth discovery while preserving privacy.

Camera-based Semantic Scene Completion (SSC) is to infer the full geometry of objects and scenes from only 2D images. The task is particularly challenging for those in-visible areas, due to the inherent occlusions and lighting ambiguity. Existing works ignore the information missing or ambiguous in those shaded and occluded areas, resulting in distorted geometric prediction. To address this issue, we propose a novel method, Bi-SSC, bidirectional geomet-ric semantic fusion for camera-based 3D semantic scene completion. The key insight is to use the neighboring structure of objects in the image and the spatial differences from different perspectives to compensate for the lack of information in occluded areas. Specifically, we introduce a spatial sensory fusion module with multiple association attention to improve semantic correlation in geometric distributions. This module works within single view and across stereo views to achieve global spatial consistency. Experimental results demonstrate that Bi-SSC outperforms state-of-the-art camera-based methods on SemanticKITTI, particularly excelling in those invisible and shaded areas.

Federated learning (FL) supports massive edge devices to collaboratively train object detection models in mobile computing scenarios. However, the distributed nature of FL exposes significant security vulnerabilities. Existing attack methods either require considerable costs to compromise the majority of participants, or suffer from poor attack success rates. Inspired by this, we devise an efficient fake node-based perception poisoning attacks strategy (FNPPA) to target such weaknesses. In particular, FNPPA poisons local data and injects multiple fake nodes to participate in aggregation, aiming to make the local poisoning model more likely to overwrite clean updates. Moreover, it can achieve greater malicious influence on target objects at a lower cost without affecting the normal detection of other objects. We demonstrate through exhaustive experiments that FNPPA exhibits superior attack impact than the state-of-the-art in terms of average precision and aggregation effect.

Compared with conventional SRAM, Spin-Transfer Torque Random Access Memory(STT-RAM) is expected to play a crucial role in future memory technologies with the increasing demands for higher storage density and lower power consumption for modern embedded systems. Moreover, Multi-Level Cell (MLC) STT-RAM outperforms Single-Level Cell (SLC) STT-RAM since it has higher bit density. However, MLC STT-RAM suffers from write performance due to the two-step state transitions (TTs) in memory cells' soft domain. State-of-the-art approaches mitigate this issue by reducing TTs with efficient data coding. Unfortunately, none of the existing works can fully eliminate the TTs. In this work, zeroTT, an optimal (3, 4)-based expansion coding method that eliminates TTs for MLC STT-RAM. The design of ZeroTT considers space overhead and coding complexity, and our experimental results demonstrate that zeroTT can completely avoid TTs, leading to a more efficient MLC STT-RAM memory in terms of access latency, energy consumption, and device lifetime.

In prenatal examinations, the fetal head circumference (HC) measurement is essential for assessing fetal weight and health conditions. The sonographers obtain the fetal HC manually by fitting peripheral skull ellipse in clinical practice, which is highly subjective, time-consuming, and experience-dependent. Recently, many fetal HC automatic measurement algorithms have been proposed to improve workflow efficiency in prenatal examination. But most automatic measurement algorithms focus on using fetal head segmentation as an intermediate processing step, and HC estimation relies heavily on segmentation results, which causes the accumulation of errors in the above two stages. Independent of the segmentation method, we design a regression network to generate the oriented bounding box to detect the head contour, and directly obtain the fetal head parameters with a pixel-based ellipse regression (PER) loss. Moreover, an effective 3D attention mechanism is integrated into the network to estimate HC more precisely without adding parameters in complex ultrasound images. The extensive experimental results on the public HC18 and our clinical dataset show that the proposed network provides a feasible scheme for end-to-end estimating fetal HC, and avoids the mistake brought by the intermediary processes.

One area of current research on adversarial attacks is how to generate plausible adversarial examples when only a small number of datasets are available. Current adversarial attack algorithms used to attack these black-box systems face a number of challenges, such as difficulty in training convergence, ambiguous sample images, substitute models collapse, unsatisfactory attack success rates, high query cost, and low defense capability improvement of target models. As a result, constructing plausible adversarial situations in a few known real-world sample circumstances remains difficult. As a solution to the aforementioned issues, this study introduces MC-Net, a novel multi-stage and multi-class balanced generating method based on a limited number of samples to generate realistic adversarial examples. Firstly, a multi-task learning approach is used to train the GAN by fully utilizing the small samples, ensuring that the size of the generated dataset for each category is balanced. In addition, we design a weight-balancing strategy to ensure faster convergence of each sub-network. Then, in the second stage, the generated samples of different categories are used to train a substitute model, and the distillation method is adopted to learn the output distribution of the target model. Finally, adversarial examples are constructed on the generated samples to complete the attack on the target models. Extensive experiments have proven that MC-Net has the following advantages: 1) The substitute model converges quickly using limited samples and queries; 2) High attack success rates can be obtained with a few queries; and 3) The constructed adversarial examples significantly improve the target model’s defense. Furthermore, we only utilize a few queries for the Microsoft Azure online model to obtain a satisfactory result. Our code can be found at https://github.com/jiaokailun/A-fast.

Adversarial attack algorithms are useful for testing and improving the robustness of industrial AI models. However, attacking black-box models with limited queries and unknown real labels remains a significant challenge. To overcome this challenge, we propose using contrastive learning to train a generated substitute model called attack contrastive learning network (ACL-Net) to attack black-box models with very few queries and no real labels. ACL-Net achieves end-to-end contrastive learning during training without labels, which differs from previous contrastive learning methods that required separate training for the classification layer with labels. We improve ACL-Net's robustness by using adversarial examples to train it during the attack stage. This approach results in more effective adversarial examples generated by ACL-Net. We conducted extensive experiments to validate the effectiveness of ACL-Net. Compared with the latest algorithms, ACL-Net requires fewer queries to achieve better attack performance, demonstrating its superiority in query-efficient black-box attacks. Overall, our approach presents a promising solution to the challenge of attacking black-box models with limited queries and unknown real labels. Our results show the effectiveness of using contrastive learning to train generated substitute models, and the potential for improving the robustness of industrial AI models through adversarial attacks.

Diffusion models have demonstrated their great ability to generate high-quality images for various tasks. With such a strong performance, diffusion models can potentially pose a severe threat to both humans and deep learning models. However, their abilities as adversaries have not been well explored. Among different adversarial scenarios, the no-box adversarial attack is the most practical one, as it assumes that the attacker has no access to the training dataset or the target model. Existing works still require some data from the training dataset, which may not be feasible in real-world scenarios. In this paper, we investigate the adversarial capabilities of diffusion models by conducting no-box attacks solely using data generated by diffusion models. Specifically, our attack method generates a synthetic dataset using diffusion models to train a substitute model. We then employ a classification diffusion model to fine-tune the substitute model, considering model uncertainty and incorporating noise augmentation. Finally, we sample adversarial examples from the diffusion models using the average approximation over the diffusion substitute model with multiple inferences. Extensive experiments on the ImageNet dataset demonstrate that the proposed attack method achieves state-of-the-art performance in both no-box attack and black-box attack scenarios.

How to construct imperceptible (realistic) fake samples is critical in adversarial attacks. Due to the sample feature diversity of a recommender system (containing both discrete and continuous features), traditional gradient-based adversarial attack methods may fail to construct realistic fake samples. Meanwhile, most recommendation models adopt click-through rate (CTR) predictors, which usually utilize black-box deep models with discrete features as input. Thus, how to efficiently construct realistic fake samples for black-box recommender systems is still full of challenges. In this article, we propose a hierarchical adversarial attack method against black-box CTR models via generating realistic fake samples, named CTRAttack. To better train the generation network, the weights of its embedding layer are shared with those of the substitute model, with both the similarity loss and classification loss used to update the generation network. To ensure that the discrete features of the generated fake samples are all real, we first adopt the similarity loss to ensure that the distribution of the generated perturbed samples is sufficiently close to the distribution of the real features, and then the nearest neighbor algorithm is used to retrieve the most appropriate features for non-existent discrete features from the candidate instance set. Extensive experiments demonstrate that CTRAttack can not only effectively attack the black-box recommender systems but also improve the robustness of these models while maintaining prediction accuracy.

Federated Learning (FL) combined with Differential Privacy (DP) is widespread in healthcare, finance, and IoT due to its advantages in multi-client data distribution. However, existing FL approaches overlook the differential impact levels among clients and data redundancy issues, resulting in high computational overhead and limited real-time applicability. Additionally, non-independent identical distribution (Non-IID) and imbalanced datasets in multi-clients pose challenges in privacy preservation and model overfitting. Therefore, we propose a balanced weight strategy for multi-stage federated learning against multi-client data skewing, called BM-FL, which involves clients, intermediate trust servers (ITSs), and the central server (CS). Firstly, to protect data privacy, an improved Laplace$\epsilon$-differential privacy method is employed. Secondly, a novel generative adversarial network (GAN) called BC-GAN is introduced. It is used to generate realistic fake samples and maintain a balanced proportion of samples across different categories. Then, to make full use of each client's valuable data, we designe a balanced weight strategy. Moreover, extensive experimental results clearly demonstrate the effectiveness of BM-FL in efficiently handling classification tasks involving Non-IID and imbalanced datasets while maintaining privacy and security. Furthermore, our method attains superior classification accuracy with fewer training epochs compared to relevant classical algorithms. The code is available athttps://github.com/ylxzjy/BMFL.git.

The construction of undetectable adversarial examples with few perturbances remains a difficult problem in adversarial attacks. At present, most solutions use the standard gradient optimization algorithm to build adversarial examples by applying global perturbations to benign samples and then launch attacks on the targets (e.g., face recognition systems). However, when the perturbance size is limited, the performance of these approaches suffers substantially. The content of crucial places in an image, on the other hand, will impact the final prediction; if these areas can be investigated and limited perturbances introduced, an acceptable adversarial example will be constructed. Based on the foregoing research, this article offers a dual attention adversarial network (DAAN) to produce adversarial examples with limited perturbations. DAAN initially searches for effective areas in an input image using the spatial attention network and channel attention network, and then creates space and channel weights. Following that, these weights direct an encoder and a decoder to generate effective perturbation, which is then combined with the input to produce an adversarial example. Finally, the discriminator determines if the created adversarial examples are true or false, and the attacked model is utilized to determine whether the generated samples fit the attack targets. Extensive studies on various datasets show that DAAN not only delivers the best attack performance across all comparison algorithms with few perturbations, but it can also significantly improve the defensiveness of the attacked models.

Many fields of scientific simulation, such as chemistry and condensed matter physics, are increasingly eschewing dense tensor contraction in favor of sparse tensor contraction. In this work, we center around binary sparse tensor contraction (SpTC) which has the challenges of index matching and accumulation. To address these difficulties, we present GSpTC, an efficient element-wise SpTC framework on CPU-GPU heterogeneous systems. GSpTC first introduces a fine-grained partitioning strategy based on element-wise tensor contraction. By analyzing and selecting appropriate dimension partitioning strategies, we can efficiently utilize the multi-threading parallelism on GPUs and optimize the overall performance of GSpTC. In particular, GSpTC leverages multi-threading parallelism on GPUs for the contraction phase and merging phase, which greatly accelerates the computation phase in sparse tensor contraction computations. Furthermore, GSpTC employs parallel pipeline technology to hide the data transmission time between the host and the device, further enhancing its performance. As a result, GSpTC achieves an average performance improvement of 267% compared to the previous state-of-the-art framework Sparta.

Convolutional neural networks (CNNs) are widely utilized in intelligent edge computing applications such as computational vision and image processing. However, as the number of layers of the CNN model increases, the number of parameters and computations gets larger, making it increasingly challenging to accelerate in edge computing applications. To effectively adapt to the tradeoff between the speed and accuracy of CNNs inference for smart applications. This paper proposes an FPGA-based adaptive CNNs inference accelerator synergistically utilizing filter pruning, fixed-point parameter quantization, and multi-computing unit parallelism called APPQ-CNN. First, the article devises a hybrid pruning algorithm based on the L1- norm and APoZ to measure the filter impact degree and a configurable parameter quantization fixed-point computing architecture instead of floating-point architecture. Then, design a cascade of the CNN pipelined kernel architecture and configurable multiple computation units. Finally, conduct extensive performance exploration and comparison experiments on various real and synthetic datasets. With negligible accuracy loss, the speed performance of our accelerator APPQ-CNN compares with current state-of-the-art FPGA-based accelerators PipeCNN and OctCNN by 2.15x and 1.91x, respectively. Furthermore, APPQCNN provides settable fixed-point quantization bit-width parameters, filter pruning rate, and multiple computation unit counts to cope with practical application performance requirements in edge computing.

Through the attention mechanism, Transformer-based large-scale deep neural networks (LSDNNs) have demonstrated remarkable achievements in artificial intelligence applications such as natural language processing and computer vision. The matrix-matrix multiplication operation (MMMO) in Transformer makes data movement dominate the inference overhead over computation. A solution for efficient data movement during Transformer inference is to embed arithmetic logic units (ALUs) into the memory array, hence an accelerator-in-memory architecture (AIMA). Existing work along this direction has not considered the heterogeneity of parallelism and resource requirements among Transformer layers. This increases the inference latency and lowers the resource utilization, which is critical for the embedded systems domain. To this end, we propose HAIMA, a hybrid AIMA and the parallel dataflow for Transformer, which exploit the cooperation between SRAM and DRAM to accelerate different MMMOs. Compared to the state-of-the-art Newton and TransPIM, our proposed hardware-software co-design achieves 1.4x-1.5x speedup, and solves the problem of resource under-utilization when DRAM-based AIMA performs the light-weight MMMOs.

Modern blockchains supporting smart contracts implement a new form of state machine replication with a trusted and decentralized paradigm. However, inefficient smart contract transaction execution severely limits system throughput and hinders the further application of blockchain.

Summary With the rapid development of the internet, cyberspace security issues have become increasingly prominent. The importance of constructing a cyberspace security system is self‐evident, but compared with attackers, defenders in cyberspace are in a castle‐like passive defense state in most cases. Therefore, building a reliable, accurate, timely, and active defense system is challenging. The key is to accurately focus on defense priorities, the anticipation of attackers who will likely succeed, and blocking attacks in a timely manner. In this article, we propose an active defense model based on the interaction of situational awareness and firewalls. First, by biasing the integrity, confidentiality, and availability of assets to get the score of assets, and using the Common Vulnerability Scoring System to assess the threat level of assets, we combine the two to determine the maximum system damage that the asset will suffer if it is lost, and then focus on defense. Meanwhile, log analysis of the network situational awareness platform can predict successful attackers, and then the linked firewall strategy can block these attacks in time before the attackers obtain attack gains. After that, we force the attackers to give up their attacks on the target by increasing the attack cost. We compared our model with iptables auto‐blocking and nginx auto‐blocking, and our model excelled them across the board in terms of comprehensiveness and false positive rate. The experimental results verify thar our active defense model proposed in this article can better reduce the defense cost and increase the attack cost, thus achieving the relatively defense goal.

In recent years, industrial control systems have evolved toward stability and efficiency, increasing industrial control systems interconnected with the Internet, which means that industrial control systems are facing more serious cyber threats. Thus, it is critical for enterprises to consider issues related to data privacy and network security. Digital twin enables real-time synchronization and simulation of data from various physical components of industrial control systems. However, anomaly detection of twin data is still challenging because existing methods are usually multi-stage with tedious training and detection steps. Therefore, we propose a method called end-to-end anomaly detection with the aim to accomplish real-time anomaly detection quickly and accurately. In order to seek key features, multidimensional deconvolutional network and attention mechanism are applied to our model. The results of this study indicate that our method performs well on precision and F1 score in comparison to the state-of-art methods.

Nowadays, more data are represented and stored by a graph structure, and subgraph matching is a fundamental problem in a variety of scientific machine learning and industrial applications, such as remote sensing image registration, industrial inspection, etc. Due to the nondeterministic polynominal hard (NP-hard) problem of subgraph matching, the explosive growth of graph data, the disadvantages of high energy consumption, and the high overhead of CPU and graphics processing unit (GPU) platforms, computing subgraph matching is becoming more and more challenging. To alleviate this problem, we propose a phased hybrid algorithm to accelerate the enumeration task of subgraph matching, calledPH-CF, based on the CPU-field programmable gate arrays (FPGA) heterogeneous platform. This approach can make full use of the pipeline and data flow mechanism, low power consumption, and configurable characteristics of FPGA. First, the matching order of query vertices automatically selects GraphQL (GQL) or RI methods according to the sparsity of the data (query) graph. Second, a candidate vertex auxiliary data structure set partitioning method is designed to effectively realize the load balance of multiple computing units at the FPGA and CPU host sides. Third, FPGA's pipeline and data flow mechanism is used to accelerate the enumeration phase of subgraph matching. Experimental results on real-world and synthetic datasets show that the performance of thePH-CFoutperforms the state-of-the-arts.PH-CFcan obtain the average performance improvement of up to$16.07\times$,$38.61\times$, and$11.46\times$over CFL, CECI, and DP-iso, respectively. Moreover, our approach has good stability and robustness on various datasets.

Face aging has attracted widespread attention in recent years, but most studies are based on the same emotional situation. Is the same person’s aging in different emotional situations the same? To solve the above confusion, this paper proposes a novel face aging model DEF-Net, which consists of two parts: different emotional learnings (Emotion-Net) and face aging (Age-Net). Given a target emotion category, DEF-Net first assists the image from the original dataset to learn the emotion features through Emotion-Net and the generated dataset is used as the inputs of Age-Net. At the same time, multiple loss functions are used to ensure that the crucial information of the original image is not lost. Secondly, Age-Net, which has been pre-trained on the original dataset, began to adopt the generated dataset to learn the aging distribution under different emotions. Designed loss functions are utilized to ensure that the realistic target images generated by Age-Net do not lose the learned emotional characteristics. Finally, extensive experiments are used to verify the performance of DEF-Net. Compared with other state-of-the-art methods: (1) DEF-Net can learn different facial emotions across different datasets and generate corresponding realistic aging images; (2) the results achieved by our DEF-Net are demonstrated to be better than those by the model that performs face aging first and then learns different emotional characteristics.

Deep learning models are widely used in daily life, which bring great convenience to our lives, but they are vulnerable to attacks. How to build an attack system with strong generalization ability to test the robustness of deep learning systems is a hot issue in current research, among which the research on black-box attacks is extremely challenging. Most current research on black-box attacks assumes that the input dataset is known. However, in fact, it is difficult for us to obtain detailed information for those datasets. In order to solve the above challenges, we propose a multi-sample generation model for black-box model attacks, called MsGM. MsGM is mainly composed of three parts: multi-sample generation, substitute model training, and adversarial sample generation and attack. Firstly, we design a multi-task generation model to learn the distribution of the original dataset. The model first converts an arbitrary signal of a certain distribution into the shared features of the original dataset through deconvolution operations, and then according to different input conditions, multiple identical sub-networks generate the corresponding targeted samples. Secondly, the generated sample features achieve different outputs through querying the black-box model and training the substitute model, which are used to construct different loss functions to optimize and update the generator and substitute model. Finally, some common white-box attack methods are used to attack the substitute model to generate corresponding adversarial samples, which are utilized to attack the black-box model. We conducted a large number of experiments on the MNIST and CIFAR-10 datasets. The experimental results show that under the same settings and attack algorithms, MsGM achieves better performance than the based models.

In this article, two Adams–Bashforth-type integration-enhanced discrete-time zeroing neural dynamic (ADTIZD) models are proposed to solve the time-varying complex Sylvester equation (TVCSE) problem in the first time. In ADTIZD models, Adams–Bashforth discrete formulas as novel discrete formulas are used, giving our ADTIZD models higher accuracy [truncation error being$O(\tau ^{5})$] but less time and space complexity than the ordinary multi-instant models. Enhanced by the integration part, the ADTIZD models can resist large additive noises, where even constant noises cannot decrease their precision. All convergence and robustness performance conclusions about our ADTIZD models are supported by rigorous theoretical proofs and numerical experiments. More comparisons between ADTIZD models and other discrete-time zeroing neural network models are shown in these experiments too. The efficacy of ADTIZD models is finally been validated in the simulation of adopting them in controlling a robotic manipulator.

The current research on adversarial attacks aims at a single model while the research on attacking multiple models simultaneously is still challenging. In this paper, we propose a novel black-box attack method, referred to as MBbA, which can attack multiple black-boxes at the same time. By encoding input image and its target category into an associated space, each decoder seeks the appropriate attack areas from the image through the designed loss functions, and then generates effective adversarial examples. This process realizes end-to-end adversarial example generation without involving substitute models for the black-box scenario. On the other hand, adopting the adversarial examples generated by MBbA for adversarial training, the robustness of the attacked models are greatly improved. More importantly, those adversarial examples can achieve satisfactory attack performance, even if these black-box models are trained with the adversarial examples generated by other black-box attack methods, which show good transferability. Finally, extensive experiments show that compared with other state-of-the-art methods: (1) MBbA takes the least time to obtain the most effective attack effects in multi-black-box attack scenario. Furthermore, MBbA achieves the highest attack success rates in a single black-box attack scenario; (2) the adversarial examples generated by MBbA can effectively improve the robustness of the attacked models and exhibit good transferability.

Modular exponentiation, an operation widely utilized in cryptographic protocols to transfer text and other forms of data, can also be applied to Internet-of-Things (IoT) devices with high security requirements. However, due to the high resource consumption of modular exponentiation, IoT devices can face the problem of resource insufficient. Fortunately, the secure outsourcing scheme offers a new solution for resource-constrained devices. In this article, we apply a parallel secure outsourcing scheme to provide the possibility for modular exponentiation operation, which is used in the IoT devices. After that, the task of modular exponentiation is decomposed and we introduce the scheme in more detail. In addition, based on this scheme, we designed an extension scheme for RSA, providing enhanced security for IoT devices. Finally, the analysis of experimental results based on 512-4096 b of data indicates the superiority in scalability and time consumption over the previous schemes.

Estimating human age is a challenging task in computer vision and most researchers are trying to make age estimation via a static facial image. However, it ignores the fact that the age of a person is the specific representation of aging. In this paper, we attempt to explore the aging/rejuvenation (AR) characteristics of faces for age estimation and we called the whole network as AR-Net. Firstly, we use GAN model for learning a manifold of the aging/rejuvenation process to a face dataset with preserving personalized face features (e.g.,gender, race). Secondly, we seek the correlated aging/rejuvenation characteristics from a narrow age interval, (e.g.,((0-100)$\rightarrow $(0-5), (5-10),…, (90-100)). Thirdly, the fine-tuned GAN is used to generate aging/rejuvenation features of all age groups and these features are applied to train corresponding ELM regressors. AR-Net is deployed on every edge server, and all AR-Nets are trained offline. Afterwards, our AR-Net is constantly updated based on the face dataset collected by the edge sensors. Finally, enormous experiments on Morph-II, CACD, and captured facial dataset have been conducted to verify the performance of our fine-tuned AR-Net and the experimental results show that the approach enhanced than the current state of the art methods.

Recent trend focuses on using heterogeneous graph of things (HGoT) to represent things and their relations in the Internet of Things, thereby facilitating the applying of advanced learning frameworks, i.e., deep learning (DL). Nevertheless, this is a challenging task since the existing DL models are hard to accurately express the complex semantics and attributes for those heterogeneous nodes and links in HGoT. To address this issue, we develop attention-aware encoder-decoder graph neural networks for HGoT, termed as HGAED. Specifically, we utilize the attention-based separate-and-merge method to improve the accuracy, and leverage the encoder-decoder architecture for implementation. In the heart of HGAED, the separate-and-merge processes can be encapsulated into encoding and decoding blocks. Then, blocks are stacked for constructing an encoder-decoder architecture to jointly and hierarchically fuse heterogeneous structures and contents of nodes. Extensive experiments on three real-world datasets demonstrate the superior performance of HGAED over state-of-the-art baselines.

Multi-task learning plays an important role in face multi-attribute prediction. At present, most researches excavate the shared information between attributes by sharing all convolutional layers. However, it is not appropriate to treat the low-level and high-level features of the face multi-attribute equally, because the high-level features are more biased toward the specific content of the category. In this article, a novel multi-attribute tensor correlation neural network (MTCN) is used to predict face attributes. MTCN shares all attribute features at the low-level layers, and then distinguishes each attribute feature at the high-level layers. To better excavate the correlations among high-level attribute features, each sub-network explores useful information from other networks to enhance its original information. Then a tensor canonical correlation analysis method is used to seek the correlations among the highest-level attributes, which enhances the original information of each attribute. After that, these features are mapped into a highly correlated space through the correlation matrix. Finally, we use sufficient experiments to verify the performance of MTCN on the CelebA and LFWA datasets and our MTCN achieves the best performance compared with the latest multi-attribute recognition algorithms under the same settings.

Although age estimation is easily affected by smiling, race, gender, and other age-related attributes, most of the researchers did not pay attention to the correlations among these attributes. Moreover, many researchers perform age estimation from a wide range of age; however, conducting an age prediction over a narrow age range may achieve better results. This article proposes a hierarchic approach referred to as EGroupNet for age prediction. The method includes two main stages, i.e., feature enhancement via excavating the correlations among age-related attributes and age estimation based on different age group schemes. First, we apply the multi-task learning model to learn multiple face attributes simultaneously to obtain discriminative features of different attributes. Second, we project the outputs of fully connected layers of several subnetworks into a highly correlated matrix space via the correlation learning process. Third, we classify these enhanced features into narrow age groups using two Extreme Learning Machine models. Finally, we make predictions based on the results of the age groups mergence. We conduct a large number of experiments on MORPH-II, LAP-2016 dataset, and Adience benchmark. The mean absolute errors of the two different settings on MORPH-II are 2.48 and 2.13 years, respectively; the normal score (ε) on the LAP-2016 dataset is 0.3578; and the accuracy of age prediction on Adience benchmark is 0.6978.

Zeroing neural network (ZNN), as a powerful calculating tool, is extensively applied in various computation and optimization fields. Convergence and noise-tolerance performance are always pursued and investigated in the ZNN field. Up to now, there are no unified ZNN models that simultaneously achieve the finite-time convergence and inherent noise tolerance for computing time-varying quadratic optimization problems, although this superior property is highly demanded in practical applications. In this paper, for computing time-varying quadratic optimization within finite-time convergence in the presence of various additive noises, a new framework for ZNN is designed to fill this gap in a unified manner. Specifically, different from the previous design formulas either possessing finite-time convergence or possessing noise-tolerance performance, a new design formula with finite-time convergence and noise tolerance is proposed in a unified framework (and thus called unified design formula). Then, on the basis of the unified design formula, a unified ZNN (UZNN) is, thus, proposed and investigated in the unified framework of ZNN for computing time-varying quadratic optimization problems in the presence of various additive noises. In addition, theoretical analyses of the unified design formula and the UZNN model are given to guarantee the finite-time convergence and inherent noise tolerance. Computer simulation results verify the superior property of the UZNN model for computing time-varying quadratic optimization problems, as compared with the previously proposed ZNN models.

Age estimation is a challenging task, because it can be easily affected by gender, race, and other intrinsic and extrinsic attributes. At the same time, performing age estimation for a narrow age range may lead to better results. In this paper, to achieve robust age estimation, an ensemble structure referred to as CNN2ELM, which includes convolutional neural network (CNN) and extreme learning machine (ELM), is proposed for age estimation. The three-level system includes feature extraction and fusion, age grouping via an ELM classifier, and age estimation via an ELM regressor. Age-Net, Gender-Net, and Race-Net are trained using different targets, such as age class, gender class, and race class, respectively, and the three networks are used to extract features corresponding to age, gender, and race from the same image of a person during validation and test stages. Features related to the age property are enhanced by fusing these of race and gender properties. Then, to achieve a narrow age range, the ELM classifies the fusion results into one of the age groups. Afterward, an age decision is made using an ELM regressor. Our network is pretrained on an ImageNet database and then fine-tuned on the IMDB-WIKI database. The recently released Adience benchmark, ChaLearn Looking at People 2016 (LAP-2016), and MORPH-II are used to verify the performance of “Race-Net + Age-Net + Gender-Net + ELM classifier + ELM regressor (RAGN).” RAGN outperforms the existing state-of-the-art age estimation methods. The mean absolute error of the age estimation of RAGN for MORPH-II is determined to be 2.61 years; the accuracy of the age estimation for the Adience benchmark is 0.6649; and the normal score (ϵ) for the sequestered test set of the LAP-2016 data set is 0.3679.

As data sets become larger and more complicated, an extreme learning machine (ELM) that runs in a traditional serial environment cannot realize its ability to be fast and effective. Although a parallel ELM (PELM) based on MapReduce to process large-scale data shows more efficient learning speed than identical ELM algorithms in a serial environment, some operations, such as intermediate results stored on disks and multiple copies for each task, are indispensable, and these operations create a large amount of extra overhead and degrade the learning speed and efficiency of the PELMs. In this paper, an efficient ELM based on the Spark framework (SELM), which includes three parallel subalgorithms, is proposed for big data classification. By partitioning the corresponding data sets reasonably, the hidden layer output matrix calculation algorithm, matrix decomposition algorithm, and matrix decomposition algorithm perform most of the computations locally. At the same time, they retain the intermediate results in distributed memory and cache the diagonal matrix as broadcast variables instead of several copies for each task to reduce a large amount of the costs, and these actions strengthen the learning ability of the SELM. Finally, we implement our SELM algorithm to classify large data sets. Extensive experiments have been conducted to validate the effectiveness of the proposed algorithms. As shown, our SELM achieves an speedup on a cluster with ten nodes, and reaches a speedup with 15 nodes, an speedup with 20 nodes, a speedup with 25 nodes, a speedup with 30 nodes, and a speedup with 35 nodes.

Summary As a parallel computation framework, Spark can cache repeatedly resilient distribution datasets (RDDs) partitions in different nodes to speed up the process of computation. However, Spark does not have a good mechanism to select reasonable RDDs to cache their partitions in limited memory. In this paper, we propose a novel selection algorithm, by which Spark can automatically select the RDDs to cache their partitions in memory according to the number of use for RDDs. Our selection algorithm speeds up iterative computations. Nevertheless, when many new RDDs are chosen to cache their partitions in memory while limited memory has been full of them, the system will adopt the least recently used (LRU) replacement algorithm. However, the LRU algorithm only considers whether the RDDs partitions are recently used while ignoring other factors such as the computation cost and so on. We also put forward a novel replacement algorithm called weight replacement (WR) algorithm, which takes comprehensive consideration of the partitions computation cost, the number of use for partitions, and the sizes of the partitions. Experiment results show that with our selection algorithm, Spark calculates faster than without the algorithm, and we find that Spark with WR algorithm shows better performance. Copyright © 2015 John Wiley & Sons, Ltd.

This paper presents a multi-objective co-evolutionary population migration algorithm based on Good Point Set (GPSMCPMA) for multi-objective optimization problems (MOP) in view of the characteristics of MOPs. The algorithm introduces the theory of good point set (GPS) and dynamic mutation operator (DMO) and adopts the entire population co-evolutionary migration, based on the concept of Pareto nondomination and global best experience and guidance. The performance of the algorithm is tested through standard multi-objective functions. The experimental results show that the proposed algorithm performs much better in the convergence, diversity and solution distribution than SPEA2, NSGA-II, MOPSO and MOMASEA. It is a fast and robust multi-objective evolutionary algorithm (MOEA) and is applicable to other MOPs.