• We propose a novel enhancing edge-aware neural network for multi-modal 3D medical image segmentation. • A feature progressive co-aggregation strategy for improving feature representation and edge voxel classification. • Compared with the most advanced methods, our model achieves better performance and generalization ability. • We construct a challenging clinical diagnostic dataset of PET images for mantle cell lymphoma. 3D segmentation is critically essential in the clinical medical field, which aids physicians in locating lesions and assists in clinical decision-making. The unique properties of organ and tumour images with large-scale variations and low-edge pixel-level contrast make clear segment edges difficult. Facing these problems, we propose an Enhancing Edge-aware Medical Image Seg mentation (E2MISeg) for smooth segmentation in boundary ambiguity. Firstly, we propose the Multi-level Feature Group Aggregation (MFGA) module to enhance the accuracy of edge voxel classification through the boundary clue of lesion tissue and background. Secondly, to minimize the influence of background noise on the model’s sensitivity to the foreground, the Hybrid Feature Representation (HFR) block utilizes an interactive CNN and Transformer to deeply mine the lesion area and edge texture features while providing more clues for the MFGA module. Finally, we introduce the Scale-Sensitive (SS) loss function that dynamically adjusts the weights assigned to targets based on segmentation errors, with these weights guiding the network to focus on regions where segmentation edges are unclear. Furthermore, we retrospectively collated the Mantle Cell Lymphoma PET Imaging Diagnosis (MCLID) dataset of 176 patients from multiple central hospitals, which enhances our algorithm’s robustness against complex clinical data. The extensive experimental results on three public challenge datasets and the MCLID clinical dataset demonstrate our approach, which outperforms the state-of-the-art methods. Further analysis shows that our components work together to achieve smooth edge segmentation, which is of great significance for accurate clinical diagnosis and prognosis analysis. The Code available at: https://github.com/SoloTillDawn/E2MISeg

The Honeynet, as a typical active defense strategy, traps attackers' malicious behaviors, changing the asymmetric situation of network attack and defense. However, a static honeynet system would create a single deception environment, making it difficult to be effective against complex attacks. Additionally, the virtual honeypots are susceptible to exploitation by attackers, potentially leading to virtual escapes. To address the above issues, we propose a game theory-based method for secure deployment of mimic honeynet to enhance the deception capabilities of a honeynet and improve the security of honeypots. We construct multi-layer mimic honeypots and dynamically schedule the business executors within the mimic honeypot according to the virtualization layer's adjudication results, effectively preventing attacker escape attempts. We construct a Bayesian game model of attack and defense for the mimic honeynet, incorporating factors such as the attractiveness of the mimic honeypot and the similarity of the business layer. The aim of this game theory model is to determine the optimal mimic honeypot deployment strategy for a dynamic honeynet. Simulation results demonstrate that the mimic honeynet proposed in this paper effectively captures more malicious attack behaviors, reduces the probability of real hosts being attacked, and strengthens the security of the honeypot itself.

Encrypted traffic classification is crucial for enhancing network management, service quality, and security. However, real-world network environments are inherently open-world scenarios in which traffic not only consists of known classes but also includes the continuous emergence of unknown classes. Existing deep learning methods typically rely on the closed-world assumption, which significantly limits their classification performance when dealing with unknown traffic types. This limitation makes it challenging to accurately classify known traffic classes and effectively identify unknown ones. Although few studies have focused on open-world scenarios, these methods often use staged strategies and struggle to reliably detect unknown traffic or to estimate novel classes. To address these challenges, we propose an end-to-end Fine-grained Encrypted traffic Classification method based on Open-set Semi-supervised Learning, called FEC-OSL. This method comprises three mutually reinforcing core components. First, we design a dual-branch flow feature extraction module to capture detailed and discriminative flow features. Second, we introduce a novel energy-based perspective that leverages energy-boundary learning to distinguish known traffic from unknown traffic, enabling precise detection of known classes. Finally, an adaptive deep clustering approach integrates feature learning with clustering to achieve fine-grained classification of unknown flows. We conduct extensive experiments on three real-world datasets, and the results validate that our proposed method exhibits outstanding performance in handling both known and unknown encrypted traffic in open-world scenarios.

In the treatment of complex diseases, drug combination therapy is common, but drug-drug interactions (DDI) can cause severe side effects, threaten patient safety, and increase healthcare costs. Existing DDI prediction methods often focus on drug substructure features but overlook the complex interactions between them. To address this, this paper proposes the Multi-dimensional Interaction Graph Neural Network (MDI-DDI). The model combines four GraphSAGE convolution layers with a Tri-Co Attention Module. It first calculates interaction strength at the 2D level using a co-attention mechanism, then captures deeper interactions at the 3D level using a triplet structure. Experimental results show that MDI-DDI outperforms existing methods, achieving ACC, AUPRC, and AUROC of 0.9613, 0.9901, and 0.9871, respectively, on the DrugBank dataset. Additionally, the risk analysis of nitrate and nitrite drugs demonstrates the model's ability to accurately identify key functional groups, further validating its interpretability.

As the demand for computation-intensive and lowlatency services grows, mobile edge computing (MEC) has been widely applied in smart devices to provide efficient and real-time assistance. However, most existing studies impose fixed assumptions and lack consideration for the uncertainty within MEC. This makes it difficult for these studies to reasonably offload tasks in complex and highly volatile scenarios. Therefore, we construct an MEC task offloading system considering multifactorial uncertainty (MECTOS-MU), which involves multiple devices and MEC servers (MSs). In MECTOS-MU, task offloading and resource allocation are jointly optimized while complying with the constraint on latency to minimize the energy consumption of all devices, which is an NP-hard problem. To address this issue, we propose a novel algorithm called distributed game offloading based on load balancing (DGOLB). This method integrates task offloading prioritization, static game theory, and load balancing to formulate efficient task offloading decisions and resource allocation schemes. Extensive simulation results demonstrate that DGOLB outperforms other baseline algorithms in terms of energy consumption, ratio of dropped tasks, and average task response time, especially in scenarios with a large number of devices.

With the development of multimedia technology, online social media has become a major medium for people to access news, but meanwhile, it has also exacerbated the dissemination of multi-modal fake news. An automatic and efficient multi-modal fake news detection (MFND) method is urgently needed. Existing MFND methods usually conduct cross-modal information interaction at later stage, resulting in insufficient exploration of complementary information between modalities. Another challenge lies in the differences among news data from different domains, leading to the weak generalization ability in detecting news from various domains. In this work, we propose an efficient Cross-modal Prompt Learning with Semantic enhancement method for Domain-robust fake news detection (CPLSD). Specifically, we design an efficient cross-modal prompt interaction module, which utilizes prompt as medium to realize lightweight cross-modal information interaction in the early stage of feature extraction, enabling to exploit rich modality complementary information. We design a domain-general prompt generation module that can adaptively blend domain-specific news features to generate domain-general prompts, for improving the domain generalization ability of the model. Furthermore, an image semantic enhancement module is designed to achieve image-to-text translation, fully exploring the semantic discriminative information of the image modality. Extensive experiments conducted on three MFND benchmarks demonstrate the superiority of our proposed approach over existing state-of-the-art MFND methods.

Honeypots based on deception technology offer a promising solution to address the asymmetry of attack and defense in the Internet of Things (IoTs). However, as the IoT security situation continues to evolve, attackers can identify honeypots by analyzing system characteristics and network behaviors, launching targeted virtual escape attacks that may exploit the honeypot as a stepping stone to compromise other systems. Once the IoT honeypot itself is successfully identified and attacked, the current defense measures typically rely on post-attack remediation. To address this challenge, we propose a mimic honeypot construction method based on incomplete information zero-sum stochastic game and Q-Learning. This method enhances the IoT honeypot’s deceptive capabilities while ensuring the security of the honeypot itself. Firstly, inspired by the concept of mimic defense, we design a dynamic heterogeneous redundancy honeypot (mimic honeypot), which contains multiple business executors composed of both business and virtualization layers. Secondly, we establish an incomplete information zero-sum stochastic game model to represent the honeypot attack-defense scenario. The Q-Learning algorithm is employed to solve for the Bayesian Nash equilibrium, enabling the mimic honeypot to adaptively adjust its deployment strategies based on the attacker’s observed actions. Finally, the experimental results demonstrate that the proposed mimic honeypot outperforms existing methods in terms of deceptive effectiveness and honeypot self-protection capabilities, significantly reducing the likelihood of honeypot compromise and ensuring robust network defense.

In the visual Internet of Things (VIoT), smart surveillance is an important component of the multimodality person retrieval (MPR) task. Capturing discriminative pedestrian information from images aids in identifying target individuals through sketch or text descriptions, improving the reliability of VIoT systems. Current granularity-level matching methods face challenges with information redundancy and modality differences in MPR tasks. In this article, we propose a novel approach for intra and intermodality saliency feature learning by multigranularity feature selection and relation (MFSR). First, to reduce redundant information within each modality, the intramodality feature selection module (IFSM) employs the adaptive weighting mechanism to enhance salient pedestrian features while suppressing irrelevant features. Second, the multigranularity feature relation module (MFRM) aligns cross-modality salient person features by increasing the similarity scores of local and global features across modalities, to reduce differences between multimodality visual-text pairs. Finally, the cross-modality similarity matching (CSM) loss is designed to enhance consistency in visual-text pairs of the same identity, ensuring compact intraclass features by minimizing discrepancies between cross-modality similarity distributions and identity-matching distributions. Experimental results show that our approach achieves state-of-the-art performance on benchmark datasets.

Mimic active defense technology effectively disrupts attack routes and reduces the probability of successful attacks by using a dynamic heterogeneous redundancy (DHR) architecture. However, current approaches often overlook the adaptability of the adjudication mechanism in complex and variable network environments, focusing primarily on system security while neglecting performance considerations. To address these limitations, we propose an output difference feedback and system benefit control based DHR architecture. This architecture introduces an adjudication mechanism based on output difference feedback, which enhances adaptability by considering the impact of each executor’s output deviation on the global decision. Additionally, the architecture incorporates a scheduling strategy based on system benefit, which models the quality of service and switching overhead as a bi-objective optimization problem, balancing security with reduced computational costs and system overhead. Simulation results demonstrate that our architecture improves adaptability towards different network environments and effectively reduces both the attack success rate and average failure rate.

Multimodal fake news detection (MFND) has received widespread attention. As a typical multimodal task, MFND is troubled by modality imbalance, where the dominant modality suppresses other modalities during the optimization process. Meanwhile, the social credibility maintains the amount of fake news less than that of real news, which causes class imbalance. We call MFND with intertwined influence of modality imbalance and class imbalance as dual imbalanced MFND, i.e., DI-MFND, which has not been well studied. In this article, we propose an approach called prototype-based fully balanced learning (PFBL) for DI-MFND. Specifically, our model contains three main parts. 1) The prototype-based modality-balanced learning (PMB) part, which constructs modality prototypes for each modality. It designs the prototype-based unimodal loss to enhance the intraclass compactness of the poorly performing modality and a constrained gradient optimization strategy to suppress the dominant modality for optimization. 2) The prototype-based class-balanced learning (PCB) part constructs class prototypes. A prototype-oriented discriminative enhancement loss is designed to effectively align samples with corresponding prototypes and increase the inter-class distance, thus enhancing the separability of different categories. 3) The multimodal fusion and classification (MFC) part employs a cross-transformer block to perform interaction between modalities and further integrates features of different modalities by attention mechanism. We propose a joint updating strategy for modality prototypes and class prototypes. Extensive experiments on three widely-used news datasets demonstrate that our approach outperforms state-of-the-art approaches.

Text-based Person Retrieval (TPR) plays a pivotal role in video surveillance systems for safeguarding public safety. As a fine-grained retrieval task, TPR faces the significant challenge of precisely capturing highly discriminative features across image and text modalities. Existing methods primarily focus on establishing modality-shared feature spaces to bridge cross-modal discrepancies. However, these methods are prone to disturbances from irrelevant information, such as background noises in the visual modality, and often over-emphasize specific local regions while neglecting the capture of diverse discriminative modal features, thereby limiting the robustness of cross-modal matching. In this paper, we introduce a novel framework, termed the Diverse Co-saliency Feature Learning Network (DCFL), which mines the co-saliency information between image and text modalities and enhances the diversity of cross-modal discriminative features while mitigating the interference of noise. Specifically, to construct cross-modal co-saliency features, we devise the Intra-modal Saliency Feature Learning (ISFL) and Cross-modal Saliency Feature Matching (CSFM) modules. ISFL employs a weighted mask mechanism to guide the model in reducing the impact of noise information in both modalities. Complementing ISFL, CSFM establishes consistent relationships between saliency features across modalities, leveraging text descriptions to align pedestrian-relevant visual regions. Furthermore, we propose the Diverse Co-saliency Feature Mining (DCFM) to bolster the diversity of discriminative co-saliency features across both image and text modalities. This module integrates a diversity regularization term, enabling the extraction of varied visual cues and capturing comprehensive features of the target individual. Extensive benchmark experiments demonstrate a substantial superiority of our approach over the state-of-the-art methods. The code will be released publicly.

Security issues in the Internet of Things (IoT) are inevitable. Uncertain threats, such as known vulnerabilities and backdoors exist within IoT, and traditional passive network security technologies are ineffective against uncertain threats. To address the above issues, we propose an active defense adjudication method based on adaptive anomaly sensing for mimic IoT. The method constructs a mimic IoT active defense architecture, improving system security and reliability despite prevailing security threats. In addition, an intelligent anomaly sensing algorithm is integrated into the adjudication module of the mimic IoT active defense architecture to support arbitration. An adaptive anomaly sensing model based on multi-feature selection is used to determine the anomaly score of the IoT device outputs, and this model fully considers the reliability of the adjudication data and improves the accuracy of the adjudication. Finally, we conduct a comparative analysis of the proposed adjudication algorithm against three others via a mimic power communication IoT system as an application scenario. The experimental results show that our algorithm can improve security and reduce the failure rate of the mimic IoT system.

With the development of the Internet and multi-media technologies, fake news spread on the Internet has become a problem that cannot be ignored by the public and the government. Multi-modal fake news detection methods have tried to make use of the powerful representation ability of graph convolutional network in the fake news detection task. However, how to learn optimal graph structure for subsequent graph learning and effectively bridge the inter-modality gap is still a challenge. In this paper, we propose a novel approach called Joint Multi-modal Graph Structure and Representation Learning (JMGSRL) for multi-modal fake news detection. It consists of two main modules, i.e., a multi-modal graph structure learning module and a modality discriminator module. The multi-modal graph structure learning module jointly optimizes the graph structures of multiple modalities based on graph convolutional network by adding the potential edges or reweighting the unreasonable edges. The optimized graph structures are further used for subsequent graph learning. To effectively deal with the modality difference issue, the modality discriminator is designed to learn modality-invariant features, and the network training is guided by the adversarial scheme. Experiments on two public real datasets demonstrate that JMGSRL can outperform the state-of-the-art related multi-modal fake news detection methods.

In recent years, the rapid proliferation of multi-modal fake news has posed potential harm across various sectors of society, making the detection of multi-modal fake news crucial. Most existing methods can not effectively reduce the redundant information and preserve both semantic and structural information. To address these problems, this paper proposes a semantic distillation and structural alignment (SDSA) network. We design an semantic distillation module for modality-specific features to preserve task-relevant semantic information and eliminate redundant information. Then, we propose a triple similarity alignment module to preserve structural information. Specifically, intra-modal similarity alignment mines intra-modal consistency by preserving the neighborhood structure within each modality, inter-modal similarity alignment explores cross-modality consistency by bringing the cross-modality feature neighborhood structures, and joint similarity alignment aims to preserve the structural information of fused features. Experiments conducted on two widely used fake news datasets demonstrate that the SDSA method outperforms state-of-the-art approaches.

Abstract Turbo codes play a crucial role in wireless communication systems, and their compiled code structures are key factors affecting the performance of the entire communication system. As a result, the study of turbo compiled code structures has been a focal point for researchers. The iterative decoding of turbo code structures has multiple limitations and large storage resource consumption, leading to poor system anti‐interference ability and a rapid increase in BER. To address these issues, this paper proposes the mimic turbo compiled code structure (MTCCS) for wireless communication systems. MTCCS is based on the DHR idea, incorporating dynamic, heterogeneous, and redundancy characteristics. Dynamicity is achieved through a dynamic scheduling algorithm based on abnormal feedback information. Heterogeneity is achieved through a codec component collection design method based on intrinsic and extrinsic heterogeneity. Redundancy is achieved through a majority voting algorithm. At the beginning of information transmission, MTCCS randomly selects heterogeneous codecs from the heterogeneous codec collection to enter the runtime pool. After the information transmission is complete, the majority voting algorithm is used to adjudicate the multi‐mode output of the codecs, resulting in a relatively accurate decoding outcome. Meanwhile, the dynamic scheduling module calculates the abnormal feedback information of each codec and accordingly dynamically schedules the mimic turbo codecs to replace the abnormal ones. Through the above process, MTCCS realizes the adaptive compilation code and improves the anti‐interference ability of turbo code. Simulation experiments are conducted on MTCCS in both non‐interference and interference scenarios. Simulation experiments show that MTCCS introducing the DHR idea achieves a balance between anti‐interference and decoding performance. It effectively addresses the issue of poor anti‐interference ability in turbo codes, and the decoding performance of MTCCS is superior to that of the previous single conventional turbo codes.

Light-detection-and-ranging-based multiobject detection and tracking play fundamental roles in autonomous driving systems. Most existing detection and tracking methods inevitably require complex pairing permutations for object association across frames, making the framework slow. Moreover, the occlusion and viewpoint changes lead to missed and false detection. To solve the abovementioned issues, this article proposes a simple joint 3-D detection and tracking approach with three-step offset learning ($\text{Offset}^{3}\text{Net}$). Specifically,$\text{Offset}^{3}\text{Net}$incorporates three task-specific output subnetworks to learn three offsets: 1) center offset, 2) motion offset, and 3) association offset. The learning of abovementioned offsets eliminates the complex bipartite matching processing. Specifically, the center offset guides the model to generate precise detections, whereas the motion offset transforms the track from the previous frame to the current frame, and the association offset minimizes the distance between detection and motion-updated track of the same object. Then, a simple read-off operation is conducted for data association on a hybrid-time centerness map, which represents the detections and offset-updated tracks. In addition, we design a detection-feature-enhanced module that captures the temporal coherence of the object motion and appearance information, avoiding the missed and false detection. Experiments on nuScenes have demonstrated the effectiveness of our$\text{Offset}^{3}\text{Net}$in terms of accuracy and speed compared with most 3-D detection and tracking methods.

Video-based visible-infrared person re-identification (VVI-ReID) aims to match the identity of a person captured in video sequences from both visible and infrared cameras. The VVI-ReID task requires considering both the spatial relationship between body parts within each frame and the temporal change of appearance between successive frames. Existing VVI Re-ID methods employ Convolutional Neural Networks to extract local spatial features and Long Short-Term Memory to form temporal associations. However, these methods can not effectively capture the global spatial feature and the long-range temporal dependencies in ultra-long sequences. In this paper, we propose a Cross-modality Spatial-temporal Transformer (CST) including a Cross-frame Tube Transformer Module (CTTM) and a Multi-frame Transformer Fusion Module (MTFM) to address these challenges. Firstly, CTTM tokenizes a video clip into multiple 3D tubes, each encapsulating local spatial-temporal information of pedestrians, and then obtains global spatial-temporal representations by establishing the relationship between tubes. Secondly, we design MTFM to exchange information between multiple frames using message tokens, thus modeling the long-range temporal dependencies of features of pedestrians. In addition, to prevent the potential representation collapse caused by triplet-based loss functions, we propose a diversity-consistency (DC) loss function to preserve the diversity and consistency of cross-modality feature representations by imposing variance, invariance, and covariance constraints in feature representations. Extensive benchmark experiments demonstrate that our approach outperforms the state-of-the-art methods with large margins.

Visible-thermal person re-identification (VT-ReID) is an image retrieval task that aims at matching the target pedestrian across the visible and thermal modalities. However, intra-class variations and cross-modality discrepancy degrade the performance of VT-ReID. Recent methods focus on extracting discriminative local features of each modality to alleviate the intra-class variations and cross-modality discrepancy, but these methods ignore semantic relations between the local features of two modalities,i.e., the spatial relations and channel relations. In this paper, we proposed a feature aggregation module (FAM) to enhance the correlation between local features including spatial dependencies and channel dependencies. Furthermore, FAM implements cross-modality feature aggregation on the enhanced features to reduce the cross-modality discrepancy. Moreover, we also proposed near neighbor cross-modality loss (NNCLoss) to mine feature consistency between modalities by constructing a cross-modality near neighbor set, which facilitates feature alignment between two modalities. Extensive experiments on two datasets demonstrate the superior performance of our approach over the existing state-of-the-arts.

Multispectral object detection for autonomous driving is multi-object localization and classification task on visible and thermal modalities. In this scenario, modality differences lead to the lack of object information in a single modality and the misalignment of cross-modality information. To alleviate these problems, most existing methods extract information based on a single scale (e.g., these methods mainly focus on detecting significant cars or pedestrians), which leads to insufficient performance in capturing multi-scale discriminative information (e.g., small bicycles and blurred pedestrians) and safety hazards in the driving process. In this paper, we propose a Multi-Scale Aggregation Network (MSANet) consisting of two parts Multi-Scale Aggregation Transformer (MSAT) and the Cross-modal Merging Fusion Mechanism (CMFM), which combined with the advantages of Transformer and CNN to extract rich image information from two modalities by mining both local and global context dependencies. Firstly, to reduce the lack of information in a single modality, we design a novel MSAT module to extract rich details and texture from multi-scale. Secondly, to alleviate feature misalignment caused by modality differences, the CMFM is utilized to aggregate complementary information on multiple levels. Comprehensive experiments on two benchmarks demonstrate that our approach shows better results than several state-of-the-art methods. The code is available athttps://github.com/ysh-strive/MSANet.

Visible-infrared person re-identification (VI-ReID) aims to match person images between the visible and near-infrared modalities. Previous VI-ReID methods are based on holistic pedestrian images and achieve excellent performance. However, in real-world scenarios, images captured by visible and near-infrared cameras usually contain occlusions. The performance of these methods degrades significantly due to the loss of information of discriminative features from the occlusion of the images. We define visible-infrared person re-identification in this occlusion scene as Occluded VI-ReID, where only partial content information of pedestrian images can be used to match images of different modalities from different cameras. In this paper, we propose a matching framework for occlusion scenes, which contains a local feature enhance module (LFEM) and a modality information fusion module (MIFM). LFEM adopts Transformer to learn features of each modality, and adjusts the importance of patches to enhance the representation ability of local features of the non-occluded areas. MIFM utilizes a co-attention mechanism to infer the correlation between each image for reducing the difference between modalities. We construct two occluded VI-ReID datasets, namely Occluded-SYSU-MM01 and Occluded-RegDB datasets. Our approach outperforms existing state-of-the-art methods on two occlusion datasets, while remains top performance on two holistic datasets.

Visible-infrared person re-identification (VI Re-ID) is designed to match person images of the same identity from visible and infrared cameras. Transformer structures have been successfully applied in the field of VI Re-ID. However, previous Transformer-based methods were mainly designed to capture global content information in a single modality, and could not simultaneously perceive semantic information between two modalities from a global perspective. To solve this problem, we propose a novel framework named the cross-modality interaction Transformer (CMIT). It has strong abilities in modeling spatial and sequential features that can capture dependencies between long-range features, and explicitly improves the discriminativeness of features by exchanging information across modalities, thus contributing to obtaining modality-invariant representations. Specifically, CMIT utilizes a cross-modality attention mechanism to enrich the feature representations of each patch token by interacting with the patch tokens of the other modality, and aggregates local features of the CNN structure and global information of the Transformer structure to mine feature saliency representation. Furthermore, the modality-discriminative (MD) loss function is proposed to learn potential consistency between modalities to encourage intra-modality compactness within class and inter-modality separation between classes. Extensive experiments on two benchmarks demonstrate that our approach outperforms state-of-the-art methods.

The rapid development of Next-Generation Sequencing (NGS) technologies has posed serious challenges to the storage and transmission of genomic data, and the bioinformatics community urgently needs efficient genome compression algorithms to support genome analysis. The existing acceleration approaches for genome compression algorithms are mostly multithreading and limited to a single machine, which cannot adapt to the demand of large-scale genome compression in the distributed environment of cloud computing. In this paper, we propose a Spark-based efficient parallel compression algorithm for FASTQ genome sequences - FQCSpark. Experimental results show that FQCSpark outperforms existing algorithms with good compression ratios by several times in speed. This is due to the fine-grained degree of parallelism design and well-designed parallel operator flow. More importantly, the degree of parallelism design in this paper is also applicable to other algorithms which compress blocks independently. Meanwhile, FQCSpark provides good compression ratios, especially on the S.cerevisiae dataset, which is 15.4% better than the latest open-source genome compression tool - Genozip. FQCSpark is the first known Spark-based parallel compression algorithm for FASTQ genome sequences.

Numerous researches on adversarial black-box attacks have proved that deep neural networks have certain insecurity. However, the current black-box attack methods still have shortages in incomplete utilization of query information. The newly proposed Simulator Attack based on meta-learning shows good performance in query-efficiency but still misses some hidden information. For this disadvantage, our research finds the usability of the feature layer output information in a simulator model for the first time. Then we propose an optimized Simulator Attack+ framework based on this discovery. By conducting experiments on the CIFAR-10 and CIFAR-100 datasets, results legibly show that Simulator Attack+ can further reduce the number of consuming queries to improve query-efficiency meanwhile maintaining attack effect. Our code is available at https://github.com/Rain117E/SimulatorAttackplus.

Since the completion of the Human Genome Project at the turn of the century, there has been an unprecedented proliferation of sequencing data. One of the consequences is that it becomes extremely difficult to store, backup, and migrate enormous amount of genomic datasets, not to mention they continue to expand as the cost of sequencing decreases. Herein, a much more efficient and scalable program to perform genome compression is required urgently. In this manuscript, we propose a new Apache Spark based Genome Compression method called SparkGC that can run efficiently and cost-effectively on a scalable computational cluster to compress large collections of genomes. SparkGC uses Spark's in-memory computation capabilities to reduce compression time by keeping data active in memory between the first-order and second-order compression. The evaluation shows that the compression ratio of SparkGC is better than the best state-of-the-art methods, at least better by 30%. The compression speed is also at least 3.8 times that of the best state-of-the-art methods on only one worker node and scales quite well with the number of nodes. SparkGC is of significant benefit to genomic data storage and transmission. The source code of SparkGC is publicly available at https://github.com/haichangyao/SparkGC .

In the author list, "Ferry Sansoto" should be Ferry Susanto.• To reflect more accurately the contribution of the article, the title should be changed to "A permutation and equinumerosity based polynomial-time algorithm for simple undirected graph isomorphism."• In the abstract, the "Pythagorean Triples Theorem" should be removed.• In the abstract, "squared sums of elements" should be "nth power sums."• In Section 2.2, "and the sum of the individual squared elements.By checking two sums," should be ", the sum of the individual squared elements and until the sum of the nth power of the nth element in the array.By checking these sums,"• In Section 2.2, "For both vertex and edge arrays of row/column sum based on the vertex and edge adjacency matrices, if and only if one array is a permutation of another one, the corresponding two graphs are isomorphic."should be "For both the vertex and edge arrays of row/column sum based on the vertex and edge adjacency matrices, if and only if one array is a permutation of another one and the corresponding edge and vertex's adjacent relationship has been preserved, the corresponding two graphs are isomorphic."

Abstract QoS‐aware based web service recommendation is one of the crucial solutions to help users find high‐quality web services. To accurately predict the QoS values of candidate services, it is usually required to collect historical QoS data of users (QoS data for short). If these collected QoS data are improperly processed, QoS data privacy may be threatened. However, how to accurately predict the QoS values of candidate services while protecting QoS data privacy has not been well studied. In response to the situation, we propose a hybrid web service recommendation mechanism, which is divided into three parts. In the first part, the QoS data privacy preservation algorithm, which called DVO, is proposed based on keeping the cosine similarity of QoS data unchanged, that is, to realize the confusion of QoS data while ensuring the availability of QoS data remains unchanged. In the second part, a hybrid matrix factorization model based on location information and service features, which called LCLMF, is proposed to improve the accuracy of QoS values prediction. According to DVO and LCLMF, the DVO + LCLMF is designed in the third part, which can accurately predict QoS values while protecting QoS data privacy. The experimental results show that DVO + LCLMF can accurately predict the QoS values of candidate services on the basis of attaining QoS data privacy protection.

Summary With the development of genome sequencing technology, the cost of genome sequencing is continuously reducing, while the efficiency is increasing. Therefore, the amount of genomic data has been increasing exponentially, making the transmission and storage of genomic data an enormous challenge. Although many excellent genome compression algorithms have been proposed, an efficient compression algorithm for large collections of FASTA genomes, especially can be used in the distributed system of cloud computing, is still lacking. This article proposes two optimization schemes based on HRCM compression method. One is MtHRCM adopting multi‐thread parallel technology. The other is HadoopHRCM adopting distributed computing parallel technology. Experiments show that the schemes recognizably improve the compression speed of HRCM. Moreover, BSC algorithm instead of PPMD algorithm is used in the new schemes, the compression ratio is improved by 20% compared with HRCM. In addition, our proposed methods also perform well in robustness and scalability. The Java source codes of MtHRCM and HadoopHRCM can be freely downloaded from https://github.com/haicy/MtHRCM and https://github.com/haicy/HadoopHRCM .

Summary In case of sharp road illumination changes, bad weather such as rain, snow or fog, wear or missing of the lane marking, the reflective water stain on the road surface, the shadow obstruction of the tree, and mixed lane markings and other signs, missing detection or wrong detection will occur for the traditional lane marking detection algorithm. In this manuscript, a lane marking detection algorithm based on high‐precision map and multisensor fusion is proposed. The basic principle of the algorithm is to use the centimeter‐level high‐precision positioning combined with high‐precision map data to complete the detection of lane markings. In the process of generating high‐precision maps or in the uncovered areas of high‐precision maps, LIDAR (LIght Detection And Ranging) is used to estimate the curvature of the road to assist in lane marking detection. The experimental results show that the algorithm has lower false detection rate in case of bad road conditions, and the algorithm is robust.

Summary The graph isomorphism problem is to determine two finite graphs that are isomorphic which is not known with a polynomial‐time solution. This paper solves the simple undirected graph isomorphism problem with an algorithmic approach as NP=P and proposes a polynomial‐time solution to check if two simple undirected graphs are isomorphic or not. Three new representation methods of a graph as vertex/edge adjacency matrix and triple tuple are proposed. A duality of edge and vertex and a reflexivity between vertex adjacency matrix and edge adjacency matrix were first introduced to present the core idea. Beyond this, the mathematical approval is based on an equivalence between permutation and bijection. Because only addition and multiplication operations satisfy the commutative law, we propose a permutation theorem to check fast whether one of two sets of arrays is a permutation of another or not. The permutation theorem was mathematically approved by Integer Factorization Theory, Pythagorean Triples Theorem, and Fundamental Theorem of Arithmetic. For each of two n ‐ary arrays, the linear and squared sums of elements were respectively calculated to produce the results.

The modality and pose variance between RGB and infrared (IR) images are two key challenges for RGB-IR person re-identification. Existing methods mainly focus on leveraging pixel or feature alignment to handle the intra-class variations and cross-modality discrepancy. However, these methods are hard to keep semantic identity consistency between global and local representation, which the consistency is important for the cross-modality pedestrian re-identification task. In this work, we propose a novel cross-modality graph reasoning method (CGRNet) to globally model and reason over relations between modalities and context, and to keep semantic identity consistency between global and local representation. Specifically, we propose a local modality-similarity module to put the distribution of modality-specific features into a common subspace without losing identity information. Besides, we squeeze the input feature of RGB and IR images into a channel-wise global vector, and through graph reasoning, the identity relationship and modality relationship in each vector are inferred. Extensive experiments on two datasets demonstrate the superior performance of our approach over the existing state-of-the-art. The code is available athttps://github.com/fegnyujian/CGRNet.

Elastic scaling is one of the techniques to deal with the sudden change of the number of tasks and the long average waiting time of tasks in the container cluster. The unreasonable resource supply may lead to the low comprehensive resource utilization rate of the cluster. Therefore, balancing the relationship between the average waiting time of tasks and the comprehensive resource utilization rate of the cluster based on the number of tasks is the key to elastic scaling. In this paper, an adaptive scaling algorithm based on the queuing model called ACEA is proposed. This algorithm uses the hybrid multiserver queuing model (M/M/s/K) to quantitatively describe the relationship among number of tasks, average waiting time of tasks, and comprehensive resource utilization rate of cluster and builds the cluster performance model, evaluation function, and quality of service (QoS) constraints. Particle swarm optimization (PSO) is used to search feasible solution space determined by the constraint relation of ACEA quickly, so as to improve the dynamic optimization performance and convergence timeliness of ACEA. The experimental results show that the algorithm can ensure the comprehensive resource utilization rate of the cluster while the average waiting time of tasks meets the requirement.

The traditional blockchain has the shortcoming that a single-chain can only deal with one or a few specific data types. The research question of how to make blockchain be able to deal with various data types has not been well studied. In this paper, we propose a single-chain based extension model of blockchain for fintech (SEBF). In the financial environment, we design a four-layer architecture for this model. By employing the external trusted or-acle group and a financial regulator agency, a variety types of data can be effectively stored in the blockchain, such that the data type extension based on a single-chain is realized. The experimental results indicate that the proposed model can improve the efficiency of simplified payment verifi-cation.

Multispectral images contain rich recognition information since the multispectral camera can reveal information that is not visible to the human eye or to the conventional RGB camera. Due to this characteristic of multispectral images, multispectral face recognition has attracted lots of research interest. Although some multispectral face recognition methods have been presented in the last decade, how to fully and effectively explore the intraspectrum discriminant information and the useful interspectrum correlation information in multispectral face images for recognition has not been well studied. To boost the performance of multispectral face recognition, we propose an intraspectrum discrimination and interspectrum correlation analysis deep network (IDICN) approach. Multiple spectra are divided into several spectrum-sets, with each containing a group of spectra within a small spectral range. The IDICN network contains a set of spectrum-set-specific deep convolutional neural networks attempting to extract spectrum-set-specific features, followed by a spectrum pooling layer, whose target is to select a group of spectra with favorable discriminative abilities adaptively. IDICN jointly learns the nonlinear representations of the selected spectra, such that the intraspectrum Fisher loss and the interspectrum discriminant correlation are minimized. Experiments on the well-known Hong Kong Polytechnic University, Carnegie Mellon University, and the University of Western Australia multispectral face datasets demonstrate the superior performance of the proposed approach over several state-of-the-art methods.

At present, there are many excellent genome compression algorithms with high genome compression ratio. However, there is a lack of highly efficient compression algorithms for simultaneous compression of a large number of genomes. This manuscript presents an algorithm, which is called FastLNGC, for simultaneous compression of a large amount of genome data based on multi-thread concurrency. This algorithm is based on the LNGC (Large Number of Genomes Compressor) algorithm, and adopts multi-thread technology to achieve concurrent processing of genome data compression. A large number of experiments show that FastLNGC has better performance on compression of a large number of genes. The source code of FastLNGC is available at https://github.com/APandaThief/FastLNGC.

Semi-supervised multi-view feature learning (SMFL) is a feasible solution for webpage classification. However, how to fully extract the complementarity and correlation information effectively under semi-supervised setting has not been well studied. In this paper, we propose a semi-supervised multi-view individual and sharable feature learning (SMISFL) approach, which jointly learns multiple view-individual transformations and one sharable transformation to explore the view-specific property for each view and the common property across views. We design a semi-supervised multi-view similarity preserving term, which fully utilizes the label information of labeled samples and similarity information of unlabeled samples from both intra-view and inter-view aspects. To promote learning of diversity, we impose a constraint on view-individual transformation to make the learned view-specific features to be statistically uncorrelated. Furthermore, we train a linear classifier, such that view-specific and shared features can be effectively combined for classification. Experiments on widely used webpage datasets demonstrate that SMISFL can significantly outperform state-of-the-art SMFL and webpage classification methods.

Stream join is one of the key operations for real-time stream data query and calculation. In light of changeable velocity of stream data, traditional static stream join methods are not so adaptive that stream data computing performance will be affected. Based on the large quantity and constantly changing velocity of stream data, by considering traditional stream join algorithm, this paper proposes an optimized algorithm for variable update cycle stream based on time window (VC-TWJoin, Variable Cycle Time Window Join). For unsteady stream calculated in stream join, the optimal update cycle will be calculated to reduce the response time of stream join and improve join efficiency and real-time capability. Both theoretical analysis and experiments demonstrate that the algorithm is better than traditional join algorithms in terms of real-time capability, join response time and throughput.

With the rapid development of Internet of Things (IOT), IOT is more and more important. Also, it faces serious security issues. This paper analyzes Mirai's architecture. The core components are C & C server and Loader server that take charge of command and control, IOT equipments are in charge of broadcast and attack. Paper analyzes Botnet propagation model, Mirais infection attack procedure, impact factor and then proposes the corresponding anti-virus strategy.