MOTIVATION: Gene expression plays a crucial role in cell function, and enhancers can regulate gene expression precisely. Therefore, accurate prediction of enhancers is particularly critical. However, existing prediction methods have low accuracy or rely on fixed multiple epigenetic signals, which may not always be available. RESULTS: We propose a two-stage framework that accurately predicts enhancers by flexibly combining multiple epigenetic signals. In the first stage, we designed a Blending-KAN model, which integrates the results of various base classifiers and employs Kolmogorov-Arnold Networks (KAN) as a meta-classifier to predict enhancers based on flexible combinations of multiple epigenetic signals. In the second stage, we developed a Stacking-Auto model, which extracted sequence features using DNABERT-2 and located the enhancers based on the Stacking strategy and AutoGluon framework. The accuracy of the Blending-KAN model reached 99.69 ± 0.11% when five epigenetic signals were used. In cross-cell line prediction, the accuracy was more significant than or equal to 93.72%. With Gaussian noise, it still maintains an accuracy of 98.74 ± 0.03%. In the second stage, the accuracy of the Stacking-Auto model is 80.50%, which is better than the existing 17 methods. The results show that our models can be flexibly used to predict and locate enhancers utilizing a combination of multiple epigenetic signals. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/emanlee/Hi-Enhancer and https://doi.org/10.6084/m9.figshare.29262158.v1.

With the widespread application of unmanned aerial vehicles (UAVs), intrusion detection based on wireless communication traffic has become increasingly critical. While deep learning (DL) models have significantly enhanced anomaly detection accuracy, recent research indicates that they remain vulnerable to adversarial sample attacks, which poses severe threats to the open communication networks used by UAV systems. To address this challenge, an enhanced UAV intrusion detection framework is proposed for defending against adversarial samples, integrating generating adversarial samples with adversarial networks (AdvGAN) and decision boundary reconstruction. Specifically, a convolutional neural network (CNN) is first developed as the baseline intrusion detection model to detect anomalous traffic in UAV wireless communication. Subsequently, to overcome the gradient-dependent perturbation limitations of traditional adversarial attack methods, AdvGAN is employed to analyze real monitored traffic data and generate semantically consistent adversarial samples that retain the intrinsic characteristics of authentic network flow. Finally, a hybrid adversarial-real data training strategy is proposed to dynamically reconstruct the model’s intrusion decision boundaries. This strategy augments the training dataset with AdvGAN-generated adversarial samples, enabling the model to adaptively adjust decision thresholds through iterative backpropagation. On a real-world dataset, the superior performance of proposed method is verified: it maintains high detection accuracy for legitimate traffic while substantially reducing the success rate of adversarial attacks. Experimental results confirm the stealthiness of generated adversarial samples and the robustness of the enhanced detection model, validating the effectiveness of the proposed framework.

The aim of camouflaged object detection (COD) is to discern concealed objects within the background. Due to issues such as high similarity to the surrounding environment, small size, occlusions, COD is considered a highly challenging task. In this paper, we propose a novel COD framework, named multi-clue sliding window attention network (MCSWA-Net), stressing in utilizing prior knowledge at different semantic levels to guide the detection of camouflaged objects via multi-scale sliding window attention (MSWA). To this end, we first devise the dynamic local detail capture (DLC) module and the global interactive decoder (GID) module to generate both local and global guidance clues. Particularly, each block of the DLC module produces local prior clue by processing corresponding image features at each stage from the encoder. And the GID module fuses all adjacent encoder features, generates global prior clue by combining fusion features of multi-semantic levels. Further, to make full use of prior clues guiding the detection of camouflaged objects at multi-semantic levels, we design the multi-scale guidance attention fusion (MAF) module and use two prior clues to refine the image features via the group fusion and the MSWA separately. Experiments conducted on four COD benchmark datasets, and results demonstrate that our MCSWA-Net is superior to state-of-the-art (SOTA) COD methods. In addition, we explore the detection capabilities of our MCSWA-Net for the downstream vision tasks related to COD, such as polyp segmentation, COVID-19 lung infection segmentation, and industrial defect detection. Experimental results show the proposed method has high degree of generality.

With the advancement of NAND flash technology, the increased storage density leads to intensified interference, which in turn raises the error rate during data retrieval. To ensure data reliability, low-density parity-check (LDPC) codes are extensively employed for error correction in NAND flash memory. Although LDPC soft decision decoding offers high error correction capability, it comes with a significant latency. Conversely, hard-decision decoding, although faster, lacks sufficient error correction strength. Consequently, flash memory typically initiates with hard-decision decoding and resorts to multiple soft decision decoding upon failure. To minimize decoding latency, this paper proposes a decoding mechanism based on the double hard decision, called DHD. This DHD scheme improves the Log-Likelihood Ratio (LLR) in the hard decision process. After the first hard decision fails, the read reference voltage (RRV) is adjusted to perform the second hard decision decoding. If the second hard decision also fails, soft decision decoding is then employed. Experimental results demonstrate that when the Raw Bit Error Rate (RBER) is$8.5 \times 10^{-3}$, DHD reduces the Frame Error Rate (FER) by 86.4% compared to the traditional method.

Recent advancements in semi-supervised learning and few-shot learning have shown significant progress in utilizing both labeled data and the unlabeled. However, most existing approaches assume the availability of at least some labeled data to establish an initial model foundation. In this study, we propose a pre-training method that integrates self-supervised information with pre-trained models, creating a more competitive and practical framework. This approach capitalizes on the strengths of powerful source domain pretrained models while effectively utilizing large-scale, real-time, unlabeled target data. Our proposed method is based on Siamese networks and leverages self-supervised information from unlabeled images. It comprises three key components: a transfer segmentation model, a Siamese network model and an image fusion model. The process begins by inputting an unlabeled image and its segmentation counterpart into the Siamese network for feature extraction. Image fusion is then performed, enabling self-supervised pre-training that enhances the performance of downstream tasks, such as image classification. We perform comprehensive experiments across various unlabeled clinical medical image datasets. The results demonstrate that our pre-trained model significantly enhances the performance of state-of-the-art semi-supervised classification models, including Mean Teacher, MixMatch, SST and SURE. Additionally, our approach strengthens the self-feature representation of unlabeled images, leading to performance gains beyond what is achievable with existing semi-supervised methods.

Building Information Model (BIM) has become a significantly digital platform for representing buildings in the Architecture, Engineering, and Construction (AEC) industry. However, the absence of extensive, class- diverse, and balanced datasets at the BIM component level has limited the development of AI-driven BIM analysis. In this study, BIMCompNet is proposed as a large-scale multimodal dataset from Industry Foundation Classes (IFC), which can learn BIM component geometry features from multiple representation methods, including rendered views, point clouds, mesh structures, voxel grids, and semantic graphs. BIMCompNet is constructed by a standardized two-stage processing pipeline: (1) At the model level, geometry units are normalized to the SI units, models are converted to the IFC format, metadata is anonymized, and components are automatically extracted into individual IFC files. (2) At the component level, semantic labels are corrected, geometry and positioning are aligned, duplicates at model and project levels are removed, and five synchronized modalities (OBJ meshes, multi-view images, point clouds, voxel grids, and heterogeneous IFC graphs) are generated. BIMCompNet comprises 1,304,206 cleaned and labeled components across 87 IFC classes, collected from 1,607 real-world BIM models spanning 14 building types. To mitigate class imbalance, underrepresented classes are merged, and dominant classes are down-sampled to create balanced subsets suitable for robust AI model training and benchmarking. Benchmarking is performed on classification tasks by different models with multiple data modalities. Both the dataset and the processing pipeline will be publicly released to support reproducibility and private dataset extension.

With the rapid development of satellite communication technology, satellite networks have become an important component of global communication infrastructure. However, satellite networks face serious security challenges, particularly in node authentication and key management. Traditional interactive authentication protocols suffer from high latency and poor reliability issues in satellite networks. To address these challenges, this paper proposes a non-interactive authentication protocol for satellite networks based on time-lock encryption and elliptic curve cryptography. A satellite communication scenario has been constructed where the protocol achieves end-to-end communication latency of less than 1.5 seconds, transmission success rate of 100%, RSA-2048 decryption processing time of only 0.1 seconds, and time-lock verification completion within 5 milliseconds. The system employs RSA-2048 keys to provide 112-bit equivalent symmetric security strength, with multi-threaded decryption services maintaining 100% availability. The protocol combines delayed decryption technology, ring signature algorithms, and device fingerprint authentication to achieve secure authentication and key agreement between satellites.

In the current era when the Internet of Things (IoT) is booming and various smart devices are closely interconnected to build a complex network system, the demand for efficient control and resource management strategies is extremely urgent. Proximal Policy Optimization (PPO), as a highly representative algorithm in deep reinforcement learning, has great potential in aspects such as precise control of IoT devices, rational resource allocation, and intelligent interaction. However, in the practical applications of the IoT, PPO mostly exists as a black-box model, which is vulnerable to adversarial attacks. Moreover, the continuous action space scenarios applicable to PPO further increase the difficulty of analysis and protection. Therefore, this paper innovatively proposes a State-Attention Adversarial Attack (S3A) for black-box PPO models in continuous action space scenarios. This method is based on the model’s attention to states, has a low cost, integrates the parts of model extraction, action clustering, and decision-making interference of the model under specific states, and has a clear interference purpose. By constructing an IoT-related victim model in the MuJoCo environment provided by the Gym library and implementing adversarial attacks, experiments have found that under four repetitions in five simulation environments, S3A reduces the final reward of the victim model by an average of 48.5%, which strongly verifies the effectiveness and influence of this attack method.

Abstract Hadoop, a distributed computing framework that can efficiently process large-scale datasets, has been used by an increasing number of organizations as the basic computing framework to build cloud computing platforms. Improving its execution efficiency is a hot research direction in the industry, and the scheduling problem is a key factor affecting the execution efficiency of Hadoop. It is very important to identify its shortcomings and improve them. This paper examines and analyses the optimization of computing task scheduling performance based on the Hadoop big data platform. This paper first analyses Hadoop big data processing. Hadoop has high scalability. Computing nodes can be added at any time, and they can participate in cluster work through simple configuration. The paper discusses the improvement in the Hadoop resource scheduling algorithm. The task scheduling algorithm in the Hadoop-based data task localization proposed in this paper is compared with the default algorithm used in the Hadoop task scheduling algorithm. The former shows better local data in all four jobs, there are more data localization tasks, and the expected goal is achieved. The effectiveness of the algorithm is verified, and the performance is improved by 30%.

Self-supervised monocular depth estimation trains by utilizing the structure of the data itself without relying on ground-truth depth labels, gaining widespread attention in fields such as autonomous driving. However, many existing methods adopt the popular encoder-decoder structure, but this has deficiencies in refining both local and global visual clues, limiting its performance in detail recovery and spatial modeling. In this paper, we propose LGEDepth, a novel method to progressively enhance local to global visual perception. In LGEDepth, we design two crucial components to comprehensively refine local and global information after the encoder-decoder. The first is the patch-wise refinement tokenizer (PWRT), which fully refines the detailed information in local regions based on a local attention strategy with low performance overhead, effectively enhancing the local visual perception of the model. The second is the hierarchical interaction module (HIM), which better aggregates multi-scale contextual information through a cross-level interaction manner while determining the optimal depth bins that adapt to the depth distribution characteristics of the scene, effectively enhancing the global visual perception of the model. The experimental results on the KITTI, Cityscapes, Make3D and RUGD datasets demonstrate that LGEDepth achieves state-of-the-art performance and exhibits strong generalization ability, outperforming existing competitors.

Camouflaged object detection aims to identify objects that blend seamlessly with their background, posing a greater challenge compared to general object detection tasks. Due to its ability to recognize camouflaged objects, such detection models hold significant practical value across various fields. To accurately identify camouflaged targets in various complex environments, we designed a dual-guided camouflaged object detection network based on boundary and texture information(BTDGNet). The process consists of two main stages. The first stage is the localization stage, which leverages a convolutional neural network (CNN) to capture boundary and texture information of objects. These features are then fused to achieve coarse localization of the camouflaged objects. In the second stage, the recognition stage, we employ a Transformer to extract global information from the image, enhancing the differentiation between foreground and background. An interactive fusion module is designed to fully exploit and integrate both global and local features, producing precise prediction images. By leveraging boundary and texture information, the model's adaptability to different camouflaged objects is improved. The integration of local and global features enhances the model's detection accuracy from various perspectives, ultimately building a camouflaged object detection model suitable for a wide range of complex scenarios. The proposed method was extensively compared with other state-of-the-art methods across four public datasets, and the results demonstrated superior performance. Furthermore, benefiting from our dual-guidance strategy that leverages both texture and boundary information, our model demonstrates robust performance. We conducted tests on detection tasks across four different domains, and the results confirm that our model can accurately segment camouflaged objects in complex scenes.

In recent years, Transformers have been gradually applied in salient object detection tasks with good results. However, the Transformer’s global modeling capabilities can lead to the loss of local details that are important in salient object detection tasks. A feature extraction backbone based on a convolutional neural network (CNN) is good at extracting local detail features due to the gradual expansion of the receptive field but is limited by the size of the receptive field, resulting in an insufficient ability to extract global semantic features. Therefore, this paper combines the Transformer with a CNN and presents a dual-branch encoder to ensure that the features extracted contain rich global semantic information as well as local detail features. In addition, due to the different features extracted by the Transformer and CNN, noise may be introduced in the fusion of the two features, so different features need to be processed correspondingly during fusion. The fusion enhancement module (FEM) we propose fuses the features of the two branches step by step. A hybrid attention mechanism is used to carry out weighted fusion of different features. This progressive approach minimizes the differences between the features of the two branches so that the merged features retain the semantic and detail features extracted by the two branches to the greatest extent. Considering the loss of detailed information caused by repeated downsampling, we propose an edge refinement module (ERM) to address the need for accurate outline prediction. This module leverages salient features to obtain edge features and gradually refines the prediction results by incorporating these edge features. It makes full use of the connection between salient features and edge features and does not introduce additional edges to extract branches. Extensive experimental evaluations conducted on five benchmark tests demonstrate the superior performance of our method compared to other existing approaches. Code can be found athttps://github.com/gfq1605694825/DSRNet-main.

Salient object detection and camouflaged object detection have attracted increasing attention due to their significant practical applications. While these two domains share similarities in recognition methods and object characteristics, they also exhibit distinctions. In this paper, we propose a novel multi-view guided network for camouflaged and salient object detection, utilizing the Transformer as the backbone network for feature extraction. Capitalizing on shared characteristics, we introduce a CNN-based multi-view encoder and a multi-view fusion module, enhancing the acquisition of multi-perspective information while minimizing the increase in computational cost. Moreover, recognizing domain differences, we incorporate an attention exploration module, seamlessly integrating multi-view features with globally extracted features from the backbone network. This integration involves simultaneous exploration from both positional and color perspectives, unearthing valuable information to identify salient and camouflaged objects. Our approach maximizes shared characteristics between the two tasks while effectively addressing their differences, leading to precise object identification—be it for camouflaged or salient objects. Extensive experiments on nine challenging benchmark datasets demonstrate the superior performance of our method across four widely used evaluation metrics, outperforming 34 state-of-the-art methods. Furthermore, we applied our method to other visually-related tasks, such as polyp segmentation and defect detection. The results further demonstrate the versatility of our model. The source code and results of our method are available athttps://github.com/1900zpf/MVGNet.

Recent studies have shown that joint depth and pose estimation using convolutional neural networks (CNNs) can learn unlabelled monocular frames. However, three problems remain: 1) CNNs can only extract local features due to the limited receptive field, 2) scale ambiguity is inherent in the monocular task, and 3) illness regions violate the photometric consistency assumption and produce large errors. We propose a novel framework, ADPDepth, with corresponding effective strategies to ameliorate the above problems. First, a PCAtt module is designed to capture the correlation between channels and efficiently extract multiscale spatial information using a multibranch parallel strategy. Second, depth-pose consistency loss is proposed based on the geometric consistency in depth and pose to constrain the scale between samples, eliminate scale ambiguity and obtain a globally consistent scale. To further improve performance, a cover mask is derived from depth-pose consistency for filtering dynamic objects and outliers to reduce the adverse effects of these illness regions. Extensive experiments are conducted on the KITTI, NYU-Depth and Make3D datasets. Based on public benchmarks, the experimental results confirm that the proposed ADPDepth framework achieves state-of-the-art performance. The effectiveness of each strategy is also verified in subsequent ablation experiments.

Laryngeal leukoplakia classification is challenging using white light endoscopy images. Relevant research focus on normal tissues versus non normal tissues, cancer versus non cancer classification. The objective of this paper is to classify laryngeal leukoplakia in white light endoscopy images into six classes: normal tissues, inflammatory keratosis, mild dysplasia, moderate dysplasia, severe dysplasia and squamous cell carcinoma. We proposed a dense multiscale convolutional neural network including parallel multiscale convolution, dense convolution and recurrent convolution in favor of extracting dense multiscale features of laryngeal leukoplakia for fine classification. The proposed network achieved an overall accuracy of 0.8958 for the six-class classification. It has high sensitivity and specificity for each class which are, respectively, 1.0000 and 0.9394 for normal tissues, 0.6667 and 1.0000 for inflammatory keratosis, 0.8889 and 0.9744 for mild dysplasia and moderate dysplasia, 0.7500 and 1.0000 for severe dysplasia, 1.0000 and 0.9767 for squamous cell carcinoma. The experimental results show that our proposed model is superior to the state-of-the-art deep learning-based models.

In an edge storage system, popular data can be stored on edge servers to enable low-latency data retrieval for nearby users. Suffering from constrained storage capacities, edge servers must process users' data requests collaboratively. For sourcing data, it is essential to find out which edge servers in the system have the requested data. In this paper, we make the first attempt to study this edge data query (EDQ) problem and present EDIndex, a distributed Edge Data Indexing system to enable fast data queries at the edge. First, we introduce a new index structure named Counting Bloom Filter (CBF) tree for facilitating edge data queries. Then, to improve query performance, we enhance EDIndex with a novel index structure named hierarchical Counting Bloom Filter (HCBF) tree. In EDIndex, each edge server maintains an HCBF tree that indexes the data stored on nearby edge servers to facilitate data sourcing between edge servers at the edge. The results of extensive experiments conducted on an edge storage system comprised of 90 edge servers demonstrate that EDIndex 1) takes up to 8.8x less time to answer edge data queries compared with state-of-the-art edge indexing systems; and 2) can be implemented in practice with a high query accuracy at low initialization and maintenance overheads.

Abstract The object tracking algorithm based on Siamese network often extracts the deep feature of the target to be tracked from the first frame of the video sequence as a template, and uses the template for the whole tracking process. Because the manually annotated target in the first frame of video sequence is more accurate, these algorithms often have stable performance. However, it is difficult to adapt to the changing target features only using the target template extracted from the first frame. Inspired by the feature fusion network based on a transformer, this paper proposes a template update module called multi‐template temporary information fusion module (MTFM), which can be trained offline. By fusing multiple target template features on time series, the template can always adapt to the changes of target appearance in the tracking process. In order to train the MTFM, this paper proposes a training method using time series data and Mean Square Error (MSE) as the loss function. This paper uses the MTFM on SiamFC++ tracker, and obtains good experimental results in three challenging datasets, including VOT2016, OTB100 and GOT‐10k. The running speed of the algorithm on graphics processing unit (GPU) is maintained at about 200fps, which exhibits good real‐time performance.

Abstract In recent years, meaningful visual image encryption schemes that the plain image is compressed and encrypted and then hidden into the carrier image have received increasing attention. This paper proposes a new meaningful visual image encryption scheme, which consists of three stages: compression (compression network)—encryption (2D‐SLC hyperchaotic map)—hiding (matrix encoding). First, the advantages of deep learning are explored. It can compress the width, height, channel, and pixel values of the plain image simultaneously. Second, a new 2D‐SLC hyperchaotic map is designed to ensure security. It has a larger chaotic space and better randomness. Finally, to obtain a high‐quality cipher image, the secure secret image is hidden in the grey carrier image by matrix encoding. The scheme can compress and encrypt the grey or colour plain image and then hide it in a grey carrier image. In addition, the theoretical peak signal‐to‐noise ratio (PSNR) between the cipher image and the carrier image is improved from 40.9292 to 42.1785 dB. The total running time is only about 0.35, 0.87 and 3.1 s for a 256 × 256, 512 × 512 and 1024 × 1024 grey or colour plain image, respectively.

As a key equipment to switch the direction of a running train, railway turnout works in complex condition which makes its fault diagnosis difficult. Generally, existing methods identify the fault by analyzing the turnout action curve acquired by sensors, which have certain practical value for fault diagnosis, but poor practicability for varied types like double or multiple action turnout. In this paper, fault detection is carried out according to the distance between the normal current curve and the test curve calculated by fast dynamic time warping algorithm. In view of the singular point problem involved, a segmentation method for current curve based on the key nodes in the turnout conversion process is proposed and applied to the fault detection of single action and double action turnouts. Experimental results show that proposed approach can effectively improve the matching accuracy of adaptive diagnosis model which is more than 96%. Furthermore, compared with the traditional dynamic time warping algorithm, the time cost can be reduced by more than 5 times.

The samples representing abnormal situation is usually very few in the dataset, which makes it difficult to learn the features of abnormal samples by machine-learning-based methods. To improve the accuracy of anomaly detection, the number of abnormal samples should be expanded to ensure the balance of the dataset. In this paper, a discrete synthetic minority oversampling technique (D-SMOTE) is proposed to generate new samples. A closed area is constructed using the three nearest abnormal samples in the dataset. The new samples are then uniformly interpolated in a closed area. By this means, the problem of the imbalance for the original dataset is handled, thus improving the data quality. Based on the expanded datasets, a two-dimensional convolutional neural network (2D CNN) is constructed to detect abnormal samples. In experiments, three cases and different machine learning methods are considered for comparison. Several indexes including accuracy, precision, confusion matrix, F1-score, and Recall have been used to evaluate the detection effectiveness. The results show that the abnormal samples can be detected accurately using oversampling data obtained from the proposed D-SMOTE method.

PIWI-interacting RNA (PiRNA) was discovered in 2006 and is expected to become a new biomarker for diagnosis and prognosis of various diseases. The purpose of this study is to explore functions of piRNAs and identify cancer subtypes on the basis of the pattern of transcriptome and somatic mutation data. A total of 285 510 SNPs in piRNAs and genes, which might affect piRNA biogenesis or piRNA targets binding were identified. Significant co-expression networks of piRNAs were then constructed separately for 12 major types of cancer. Finally, mutational matrices were mapped to piRNA network, propagated, and clustered for identification of cancer-related piRNAs and cancer subtypes. Findings showed that subtypes of three types of cancer (COAD, STAD and UCEC), which are significantly associated with survival were identified. Analysis of differentially expressed piRNAs in UCEC subtypes showed that piRNA function is closely related to cancer hallmarks “Enabling Replicative Immortality” and contributes to initiation of cancer.

One of the integral parts of the infrared search and tracking (IRST) system is the infrared small target detection. To facilitate the detection of infrared small targets in complex backgrounds, infrared small target detection based on the weighted double local contrast measure (WDLCM) utilizing a novel window detection framework is proposed. First, a novel window is designed to measure the weighted double local contrast. Second, a double local contrast measure (DLCM) is proposed to enhance the region of infrared small targets in infrared images. It consists of the local contrast between the target region and surrounding background region and that within the target region. Then, a weighting function (W) is established to further enhance targets and suppress surrounding backgrounds by exploiting the variance of the target region, the standard deviation of the surrounding background region, and the difference variance between the target region and surrounding background region. Finally, after obtaining a WDLCM saliency map, adaptive threshold segmentation will be employed in order to capture real targets. The experimental results on different scene datasets show that the proposed method has a high detection rate, a low false alarm rate and good real-time performance compared with existing methods.

Deep hashing methods have shown their superiority to traditional ones. However, they usually require a large amount of labeled training data for achieving high retrieval accuracies. We propose a novel transductive semisupervised deep hashing (TSSDH) method which is effective to train deep convolutional neural network (DCNN) models with both labeled and unlabeled training samples. TSSDH method consists of the following four main ingredients. First, we extend the traditional transductive learning (TL) principle to make it applicable to DCNN-based deep hashing. Second, we introduce confidence levels for unlabeled samples to reduce adverse effects from uncertain samples. Third, we employ a Gaussian likelihood loss for hash code learning to sufficiently penalize large Hamming distances for similar sample pairs. Fourth, we design the large-margin feature (LMF) regularization to make the learned features satisfy that the distances of similar sample pairs are minimized and the distances of dissimilar sample pairs are larger than a predefined margin. Comprehensive experiments show that the TSSDH method can produce superior image retrieval accuracies compared to the representative semisupervised deep hashing methods under the same number of labeled training samples.

Whether the railway track can run smoothly for a long time directly affects the safety of the railway. In view of the nonlinear, random, and sudden characteristics of the time series data of railway track, we propose a WT-GA-GRU model to forecast the track irregularity of high-speed railway. Firstly, the original time series is decomposed by wavelet transform (WT), and the decomposed multiple time series are forecasted by the gated recurrent unit (GRU) networks optimized by genetic algorithm (GA). Then the forecasted results are obtained by wavelet reconstruction. Experimental results show that, compared with support vector machine (SVM) and long short-term memory (LSTM) model, the combined WT-GA-GRU model proposed in this paper has higher forecasting accuracy.

Internet of Things (IoT) is the development and extension of computer, Internet, and mobile communication network and other related technologies, and in the new era of development, it increasingly shows its important role. To play the role of the Internet of Things, it is especially important to strengthen the network communication information security system construction, which is an important foundation for the Internet of Things business relying on Internet technology. Therefore, the communication protocol between IoT devices is a point that cannot be ignored, especially in recent years; the emergence of a large number of botnet and malicious communication has seriously threatened the communication security between connected devices. Therefore, it is necessary to identify these unknown protocols by reverse analysis. Although the development of protocol analysis technology has been quite mature, it is impossible to identify and analyze the unknown protocols of pure bitstreams with zero a priori knowledge using existing protocol analysis tools. In this paper, we make improvements to the existing protocol analysis algorithm, summarize and learn from the experience and knowledge of our predecessors, improve the algorithm ideas based on the Apriori algorithm idea, and perform feature string finding under the idea of composite features of CFI (Combined Frequent Items) algorithm. The advantages of existing algorithm ideas are combined together to finally propose a more efficient OFS (Optimal Feature Strings) algorithm with better performance in the face of bitstream protocol feature extraction problems.

Conventionally, the transition probabilities in the interacting multiple model (IMM) are often fixed based on the prior information. However, this conservative setting may result in inaccurate state estimations. To solve this problem, a Bayesian-based online correction function is proposed in this paper, which can adaptively adjust the transition probabilities. To deal with the response lag and the short-term peak estimation error problem during the respond to model jump, a model jumping threshold is defined, so that the current information of the models can be fully utilized by the IMM algorithm and the correction function of the transition probabilities can be further improved. Subsequently, an adaptive transition probability-based parallel IMM algorithm is proposed in this paper. Finally, three maneuvering target tracking simulations are conducted to verify the performance of the proposed algorithm, the results show that the proposed algorithm can improve the response speed of the system model jump and the state estimation accuracy. The effectiveness and feasibility of the algorithm are proven.

Android system has been one of the main targets of hacker attacks for a long time. At present, it is faced with security risks such as privilege escalation attacks, image tampering, and malicious programs. In view of the above risks, the current detection of the application layer can no longer guarantee the security of the Android system. The security of mobile terminals needs to be fully protected from the bottom to the top, and the consistency test of the hardware system is realized from the hardware layer of the terminal. However, there is not a complete set of security measures to ensure the reliability and integrity of the Android system at present. Therefore, from the perspective of trusted computing, this paper proposes and implements a trusted static measurement method of the Android system based on TrustZone to protect the integrity of the system layer and provide a trusted underlying environment for the detection of the Android application layer. This paper analyzes from two aspects of security and efficiency. The experimental results show that this method can detect the Android system layer privilege escalation attack and discover the rootkit that breaks the integrity of the Android kernel in time during the startup process, and the performance loss of this method is within the acceptable range.

The prediction of flight delays plays a significantly important role for airlines and travellers because flight delays cause not only tremendous economic loss but also potential security risks. In this work, we aim to integrate multiple data sources to predict the departure delay of a scheduled flight. Different from previous work, we are the first group, to our best knowledge, to take advantage of airport situational awareness map, which is defined as airport traffic complexity (ATC), and combine the proposed ATC factors with weather conditions and light information. Features engineering methods and most state-of-the-art machine learning algorithms are applied to a large real-world data sources. We reveal a couple of factors at the airport which has a significant impact on flight departure delay time. The prediction results show that the proposed factors are the main reasons behind the flight delays. Using our proposed framework, an improvement in accuracy for flight departure delay prediction is obtained.

An effective and scalable metaheuristic algorithm termed Phase Based Optimization (PBO) for solving big optimization problems is proposed. In the natural system, the individuals with three phases which are gas phase, liquid phase and solid phase have completely different motional characteristics. PBO mimics the above three kinds of motional characteristics of individuals, and three corresponding operators, diffusion operator of gas individuals, flowing operator of liquid individuals and perturbation operator of solid individuals are devised. The diffusion operator and the flowing operator are utilized to perform the task of divergence and convergence respectively, and the perturbation operator plays a role of fine-tune search. Despite its algorithmic simplicity, PBO can effectively find a very better solution even in a high dimensional search space. The experimental results demonstrate that PBO can provide much better accuracy on optimized solutions and lower time complexity than the other state-of-the-art optimization algorithms.

In recent years, community detection has become a hot research topic in complex networks. Many of the proposed algorithms are for detecting community based on the modularity Q. However, there is a resolution limit problem in modularity optimization methods. In order to detect the community structure more effectively, a memetic particle swarm optimization algorithm (MPSOA) is proposed to optimize the modularity density by introducing particle swarm optimization-based global search operator and tabu local search operator, which is useful to keep a balance between diversity and convergence. For comparison purposes, two state-of-the-art algorithms, namely, meme-net and fast modularity, are carried on the synthetic networks and other four real-world network problems. The obtained experiment results show that the proposed MPSOA is an efficient heuristic approach for the community detection problems.

Botnet has become a popular technique for deploying Internet crimes. The command of botnet has evolved into a major way for attackers to launch Distributed Denial of Service attacks on network servers. Modelized analysis methods need to be studied for botnet attacks implements, defense, and prediction. In this paper, we propose a novel game theory-based model to describe the scenario, in which the botmaster launching Distributed Denial of Service attacks using a botnet while the defender equipped a firewall defending. In our model, we consider the following: firstly, the botmaster and the defender can be rational or irrational; secondly, the interaction between the botmaster and the defender is modeled as a dynamic game; thirdly, their supporting or not self-learning databases. We detail the analysis of eight sub-scenarios for the assumptions and give an easy-to-use algorithm for adjustment of offensive and defensive strategy. We use the OPNET to validate our model and its effectiveness. The experiment result shows that our strategy can improve the firewall abilities to lower false alarm rate FR and improve the botmaster lower exposure rate of botnet to avoid detection. Furthermore, the model is helpful to evaluate defense ability of the defender towards current botmaster attacks by analyzing attack log in sandbox. Copyright © 2016 John Wiley & Sons, Ltd.

Cooperative Coevolution (CC) was introduced into evolutionary algorithms as a promising framework for tackling large scale optimization problems through a divide-and-conquer strategy. A number of decomposition methods to identify interacting variables have been proposed to construct subcomponents of a large scale problem, but if the variables are all non-separable, all the CC-based algorithms of decomposition will lose the functionality, therefore, classical CC-based algorithms are inefficient in processing non-separable problems that have many interacting variables. In this paper, a new CC framework which integrates global and local search algorithms is proposed for solving large scale optimization problems. In the stage of global cooperative coevolution, we introduce a new interacting variables grouping method named Sequential Sliding Window. When the performance of global search reaches a deviation tolerance or the variables are fully non-separable, we then use a more effective local search algorithm to subsequently search the solution space of the large scale optimization problem. The integration of global and local algorithms into CC framework can efficiently improve the capability in processing large scale non-separable problems. Experimental results on large scale optimization benchmarks show that the proposed framework is more effective than other existing CC frameworks.

Optimal approximation of linear system models is an important task in the controller design and simulation for complex dynamic systems. In this paper, we put forward a novel nature-based meta-heuristic method, called artificial raindrop algorithm, which is inspired from the phenomenon of natural rainfall, and apply it for optimal approximation of a stable linear system. It mimics the changing process of a raindrop, including the generation of raindrop, the descent of raindrop, the collision of raindrop, the flowing of raindrop and the updating of raindrop. Five corresponding operators are designed in the algorithm. Numerical experiment is carried on the optimal approximation of a typical stable linear system in two fixed search intervals. The result demonstrates better performance of the proposed algorithm comparing with that of other five state-of-the-art optimization algorithms.

This paper proposes a novel strategy for formally analyzing functional requirements specification (FRS) and applies it to the Automatic Train Protection and Block (ATPB) system, which is proposed to reconstruct conventional rail lines in Japan. Based on the FRS in natural language, firstly, dynamic state transitions are extracted to express the operational mechanisms and determine the system parameters. A complete model of the ATPB system is then established using Unified Modeling Language (UML) to express the system structure graphically and explicitly. After achieving a common understanding, a VDM++ model is established formally to redescribe the original FRS of the ATPB system which is written in natural language (i.e. Japanese). Following that, in order to ensure internal consistency of the specification, proof obligations of the VDM++ model are discharged. Furthermore, a comprehensive testing is implemented to ensure that the FRS meets actual requirements. Finally, the system is simulated strictly in accordance with the formal specification. Without any runtime errors, collisions or derailments, the results of the simulation demonstrate the high quality and safety of the specification.