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In a service ecosystem, the trust of users in services serves as the foundation for maintaining normal interactions among users, service providers, and platforms. However, malicious attacks can tamper with the trust value of these services, making it difficult for users to identify reliable services and undermining the benefits of reliable service providers and platforms. When existing trust management models address the impact of malicious attacks on service reliability, they rarely consider leveraging different attack targets to improve the accuracy of compromised service trust. Therefore, we propose a trust enhancement model based on distributed learning and blockchain in the service ecosystem, which adaptively enhances the trust values of compromised services according to the targets of anomalous attacks. Firstly, we conduct a comprehensive analysis of the targets of malicious attacks using distributed learning. Secondly, we introduced a trust enhancement contract that utilizes different methods to enhance the trust of the service based on various attack targets. Finally, our approach outperforms the baseline method significantly. For different attack targets, we observe a reduction in RMSE by 12.38% and 12.12%, respectively, and an enhancement in coverage by 24.94% and 14.56%, respectively. The experimental results show the reliability and efficacy of our proposed model.

Grinding experiments of coal are designed to study the influence of spherical and cylindrical media on the three-dimensional morphology characteristics of grinding products. Details of experiments consist of coal grinding, screening of grinding produces, non-destructive testing of coarse-grained coal based on high-resolution three-dimensional X-ray microscopic imaging system, and particle segmentation extraction and three-dimensional morphology feature parameter calculation based on the Dragonfly software. For the low rotation rate condition of the ball mill, if the total surface area or total mass of grinding media is the same, breakage rate of the top size particles ground by the grinding ball is higher than that of the cylindrical media. Moreover, the yield of −1 mm fines yielded by the ball is the highest. The grinding effect reduces the volume and surface area of coarse materials. For the three grinding conditions, the steel cylindrical media with the largest surface area has a more significant “grinding and stripping” effect on coal and the highest particle sphericity.

The sixth-generation (6G) network is expected to provide both communication and sensing (C&S) services. However, spectrum scarcity poses a major challenge to the harmonious coexistence of C&S systems. Without effective cooperation, the interference resulting from spectrum sharing impairs the performance of both systems. This paper addresses C&S interference within a distributed network. Different from traditional schemes that require pilot-based high-frequency interactions between C&S systems, we introduce a third party named the radio map to provide the large-scale channel state information (CSI). With large-scale CSI, we optimize the transmit power of C&S systems to maximize the signal-to-interference-plus-noise ratio (SINR) for the radar detection, while meeting the ergodic rate requirement of the interfered user. Given the non-convexity of both the objective and constraint, we employ the techniques of auxiliary-function-based scaling and fractional programming for simplification. Subsequently, we propose an iterative algorithm to solve this problem. Simulation results corroborate our idea that the extrinsic information, i.e., positions and surroundings, is effective to decouple C&S interference.

Quick response to disasters is crucial for saving lives and reducing loss. This requires low-latency uploading of situation information to the remote command center. Since terrestrial infrastructures are often damaged in disaster areas, non-terrestrial networks (NTNs) are preferable to provide network coverage, and mobile edge computing (MEC) could be integrated to improve the latency performance. Nevertheless, the communications and computing in MEC-enabled NTNs are strongly coupled, which complicates the system design. In this paper, an edge information hub (EIH) that incorporates communication, computing and storage capabilities is proposed to synergize communication and computing and enable systematic design. We first address the joint data scheduling and resource orchestration problem to minimize the latency for uploading sensing data. The problem is solved using an optimal resource orchestration algorithm. On that basis, we propose the principles for resource configuration of the EIH considering payload constraints on size, weight and energy supply. Simulation results demonstrate the superiority of our proposed scheme in reducing the overall upload latency, thus enabling quick emergency rescue.

To tackle the challenge of inefficient utilization of spatiotemporal spectrum resources in heterogeneous networks, a reliable spectrum sharing strategy based on game theory was proposed for scenarios where multiple secondary users dynamically access through sensing spatiotemporal spectrum opportunities.Firstly, secondary users sensed the activity state of primary users through channel sampling and subsequently seek suitable spatiotemporal spectrum opportunities.Each secondary user aimed to access more spectrum resources while preventing quality degradation between primary users.Consequently, a negotiation of individual utilities between secondary and primary users was needed, in order to reach a mixed Nash equilibrium.When the secondary user's transmission power was abnormal, its behavior would be regarded as a violation.Then, a backoff time compensation algorithm was presented to penalize the secondary users, according to the number of violations and active nodes in the network.Finally, simulation results validate the effectiveness of the spatiotemporal spectrum opportunities sharing strategy with the penalty mechanism.

Simultaneous localization and mapping (SLAM) is an essential component for smart robot operations in unknown confined spaces such as indoors, tunnels and underground. This paper proposes a novel tightly-coupled ranging-LiDAR-inertial simultaneous localization and mapping framework, namely RLI-SLAM, which is designed to be high-accuracy, fast and robust in the long-term fast-motion scenario, and features two key innovations. The first one is tightly fusing the ultra-wideband (UWB) ranging and the inertial sensor to prevent the initial bias and long-term drift of the inertial sensor so that the point cloud distortion of the fast-moving LiDAR can be effectively compensated in real-time. This enables high-accuracy and robust state estimation in the long-term fast-motion scenario, even with a single ranging measurement. The second one is deploying an efficient loop closure detection module by using an incremental smoothing factor graph approach, which seamlessly integrates into the RLI-SLAM system, and enables high-precision mapping in a challenging environment. Extensive benchmark comparisons validate the superior accuracy of the proposed new state estimation and mapping framework over other state-of-the-art systems at a low computational complexity, even with a single ranging measurement and/or in a challenging environment.

To meet the needs of information security sharing and systematic dynamic cooperation of manned and unmanned formations and to build a dynamic communication network, firstly, a networked high-precision system time synchronization scheme was designed, using integrated network link management strategy and wireless channel on-demand preference algorithm, and the time synchronization accuracy could be up to 100 nanosecond level.Secondly, a data privacy protection and secure sharing solution for airborne networks was built, implementing communication data security of manned and unmanned formation, and analyzing and discussing the performance and security to prove that the proposed solution has a provable high security strength.Meanwhile, a dynamic cooperation model of manned and unmanned formation was designed to discover, authenticate and revoke each node in real time, perform legitimacy identity verification, optimize allocation scheme for multi-objective information acquisition through multi-machine collaborative construction, differentiated data was used to transmit synchronize data, save network resources, improve data transmission efficiency, and meet the demand of systematic cooperative operations.

Federated Learning (FL) can train models in an edge environment without sending raw data. However, the performance is still constrained by data heterogeneity. To address the problems of data heterogeneity and resource scarcity in edge devices, we propose Federal Learning via Dynamic Aggregation (FedDA), which eliminates the influence of data heterogeneity and improves model accuracy. FedDA updates the impact of individual local models on the global model in real-time at different stages. It adjusts the local epoch in each round to prevent the device from dropping out while obtaining a more accurate local model. The core module is the model impact factor (MIF) that inscribes the aggregation weights to solve the impact of fixed weights on the aggregation model with improper extraction of local information. We conducted several experiments to evaluate the convergence speed using different algorithms on the MINIST. FedDA consistently outperforms the other six SOTA algorithms on MNIST, Cifar10, and Cifar100 datasets. In significant data heterogeneity, FedDA improves accuracy by up to 6% over the different algorithms and at least about 3%, especially in resource-scarce environments. To reach the specified accuracy, FedDA is 3 times faster than SCAFFOLD and at least 50% faster than other algorithms.

Abstract The rise of 5G technology has driven the development of edge computing. Computation offloading is the key and challenging point in edge computing, which investigates offloading resource-intensive computing tasks from the user side to the cloud or edge side for processing. More consideration needs to be given to load balancing, user variability, and the heterogeneity of edge facilities in relevant research. In addition, most of the research around edge collaboration also revolves around cloud-side collaboration, which pays relatively little attention to the collaboration process between edge nodes, and the incentive and trust issues of the collaboration process need to be addressed. In this paper, we consider the impact of the user demand variability and the edge facility heterogeneity, then propose a method based on Vickrey-Clarke-Groves (VCG) auction theory to accommodate the edge demand response (EDR) process where the number of users and service facilities do not match. The method makes users’ bidding rules satisfy the Nash equilibrium and weakly dominant strategy, which can improve the load balancing of edge nodes, has positive significance in improving the edge resource utilization and reducing the system energy consumption. In particular, combined with blockchain, we further optimize the incentive and trust mechanism of edge collaboration and consider three scenarios: no collaboration, internal collaboration, and incentive collaboration. We also consider the impact of the user task’s transmission distance on the quality of experience (QoE). In addition, we illustrate the possible forking attack of blockchain in collaborative edge computing and propose a solution. We test the performance of the proposed algorithm on a real-world dataset, and the experimental results verify the algorithm’s effectiveness and the edge collaboration’s necessity.

In the sixth generation (6G) era, intelligent machine network (IMN) applications, such as intelligent transportation, require collaborative machines with communication, sensing, and computation (CSC) capabilities. This article proposes an integrated communication, sensing, and computation (ICSAC) framework for 6G to achieve the reciprocity among CSC functions to enhance the reliability and latency of communication, accuracy and timeliness of sensing information acquisition, and privacy and security of computing to realize the IMN applications. Specifically, the sensing and communication functions can merge into unified platforms using the same transmit signals, and the acquired real-time sensing information can be exploited as prior information for intelligent algorithms to enhance the performance of communication networks. This is called the computing-empowered integrated sensing and communications (ISAC) reciprocity. Such reciprocity can further improve the performance of distributed computation with the assistance of networked sensing capability, which is named the sensing-empowered integrated communications and computation (ICAC) reciprocity. The above ISAC and ICAC reciprocities can enhance each other iteratively and finally lead to the ICSAC reciprocity. To achieve these reciprocities, we explore the potential enabling technologies for the ICSAC framework. Finally, we present the evaluation results of crucial enabling technologies to show the feasibility of the ICSAC framework.

As a special type of mobile ad hoc network (MANET), the flying ad hoc network (FANET) has the potential to enable a variety of emerging applications in both civilian wireless communications (e.g., 5G and 6G) and the defense industry. The routing protocol plays a pivotal role in FANET. However, when designing the routing protocol for FANET, it is conventionally assumed that the aerial nodes move randomly. This is clearly inappropriate for a mission-oriented FANET (MO-FANET), in which the aerial nodes typically move toward a given destination from given departure point(s), possibly along a roughly deterministic flight path while maintaining a well-established formation, in order to carry out certain missions. In this paper, a novel cyber–physical routing protocol exploiting the particular mobility pattern of an MO-FANET is proposed based on cross-disciplinary integration, which makes full use of the mission-determined trajectory dynamics to construct the time sequence of rejoining and separating, as well as the adjacency matrix for each node, as prior information. Compared with the existing representative routing protocols used in FANETs, our protocol achieves a higher packet-delivery ratio (PDR) at the cost of even lower overhead and lower average end-to-end latency, while maintaining a reasonably moderate and stable network jitter, as demonstrated by extensive ns-3-based simulations assuming realistic configurations in an MO-FANET.

With the mutual interaction and dependence of several intelligent services, a crowd intelligence service network has been formed, and a service ecosystem has gradually emerged. Such a development produces an ever-increasing effect on our lives and the functioning of the whole society. These facts call for research on these phenomena with a new theory or perspective, including what a smart society looks like, how it functions and evolves, and where its boundaries and challenges are. However, the research on service ecosystems is distributed in many disciplines and fields, including computer science, artificial intelligence, complex theory, social network, biological ecosystem, and network economics, and there is still no unified research framework. The researchers always have a restricted view of the research process. Under this context, this paper summarizes the research status and future developments of service ecosystems, including their conceptual origin, evolutionary logic, research topic and scale, challenges, and opportunities. We hope to provide a roadmap for the research in this field and promote sound development.

Abstract Background The laboratory mouse was domesticated from the wild house mouse. Understanding the genetics underlying domestication in laboratory mice, especially in the widely used classical inbred mice, is vital for studies using mouse models. However, the genetic mechanism of laboratory mouse domestication remains unknown due to lack of adequate genomic sequences of wild mice. Results We analyze the genetic relationships by whole-genome resequencing of 36 wild mice and 36 inbred strains. All classical inbred mice cluster together distinctly from wild and wild-derived inbred mice. Using nucleotide diversity analysis, Fst, and XP-CLR, we identify 339 positively selected genes that are closely associated with nervous system function. Approximately one third of these positively selected genes are highly expressed in brain tissues, and genetic mouse models of 125 genes in the positively selected genes exhibit abnormal behavioral or nervous system phenotypes. These positively selected genes show a higher ratio of differential expression between wild and classical inbred mice compared with all genes, especially in the hippocampus and frontal lobe. Using a mutant mouse model, we find that the SNP rs27900929 (T>C) in gene Astn2 significantly reduces the tameness of mice and modifies the ratio of the two Astn2 (a/b) isoforms. Conclusion Our study indicates that classical inbred mice experienced high selection pressure during domestication under laboratory conditions. The analysis shows the positively selected genes are closely associated with behavior and the nervous system in mice. Tameness may be related to the Astn2 mutation and regulated by the ratio of the two Astn2 (a/b) isoforms.

The traditional industrial chain consists of a group of entities that are familiar with each other, and the relationship between the entities is maintained by signing contracts. However, on the one hand, the inability to reach mutual trust between unfamiliar entities limits the collaboration of entities, and on the other hand, most of the profits in the industrial chain are grabbed by the dominant enterprises. In this paper, an information system for collaboration among business entities is developed based on blockchain, based on which a business model called Decentralized Industry Alliance (DIA) is proposed. Transactions and profit distribution in the DIA are executed by smart contracts, which enables unfamiliar entities to trust each other without relying on a third party. Any entity that voluntarily complies with the smart contracts automatically becomes a member of the DIA, thereby promoting the expansion of the industry scale. The smart contracts distribute profits according to each entity’s contribution to the alliance, so as to ensure the fairness of value distribution. A case about the alliance of elderly care industry is carried out, which shows that the DIA achieves rapid expansion on the premise of ensuring fairness. As a general business model in the web 3.0 era, the DIA can be referenced by other industries.

Water resources management is a challenging task caused by huge uncertainties and complexities in hydrological processes and human activities. Over the last three decades, various scholars have carried out the study on hydrological simulation under complex conditions and quantitatively characterized the associated uncertainties for water resources systems. To keep abreast of the development of the collective knowledge in this field, a scientometric review and metasynthesis of the existing uncertainty analysis research for supporting hydrological modeling and water resources management has been conducted. A total of 2020 publications from 1991 to 2018 were acquired from the Web of Science. The scientific structure, cooperation, and frontiers of the related domain were explored using the science mapping software CiteSpace V5.4.R3. Through co–citation, collaboration, and co–occurrence network study, the results present the leading contributors among all countries and hotspots in the research domain. In addition, synthetical uncertainty management for hydrological models and water resource systems under climatic and land use change will continue to be focused on. This study comprehensively evaluates various aspects of uncertainty analysis in hydrologic simulation–optimization systems, showcasing advanced data analysis and artificial intelligence technologies. It focuses on current research frontiers, aiding decision–makers in better understanding and managing the complexity and uncertainties of water resource systems, thereby enhancing the sustainability and efficiency of responses to environmental changes.

【Objective】The study was carried out to understand the rodent infestation in typical farmlands and the difference of rodent community structure in different habitats of Guangdong Province, with a view to providing basis for the prevention and control of rodents in Guangdong.【Method】Night trapping method was used to monitor rodents for three consecutive years at two monitoring sites: Nanxiong City in northern Guangdong and Xinhui District in Pearl River Delta. Quantitative analysis of the rodent species composition, population size and community biomass structure was conducted. In addition, the differences of rodent community structures in different habitats were compared.【Result】The total captured rate in 3 years was 6.37%. A total of 836 captured individuals belonged to 5 species of rodents. Both captured number (589 individuals) and biomass (322.34 g/100 trap nights) of Rattus losea were dominant in those 5 species. The basic species composition of rodent community in farmlands of monitoring sites was"Rattus losea + Bandicota indica + Mus caroli + Rattus norvegicus + Rattus tanezumi". The monthly dynamics of rodent population showed a bimodal pattern, with two peaks (in January and November) and two valleys (in April and July) every year. The highest rodent population density season was winter, and the average captured rate was 7.17 (±2.49)%. While the spring was the lowest rodent population density season with the average captured rate of 4.86 (±2.21)%. The rodent population density and biomass at Nanxiong monitoring site (8.38%±5.05%, 727.92 g/100 trap nights) were significantly higher than those at Xinhui monitoring site (4.88%±4.28%, 472.52 g/100 trap nights). There also were significant differences in species composition, population size and community biomass structure between the two monitoring sites.【Conclusion】There are significant differences in farmland rodent population size in different months, different seasons, and at different sites. To achieve sustainable rodent pest management under the new circumstances, it is needed to enhance the accuracy of monitoring and early warning, and to constantly adjust the control strategies according to the changing planting structure and habitat conditions.

To meet the requirements of machine communication and immersive service in the 6G era for high precision perception and high speed communication, communication perception integration technology emerges at the right moment.Based on this, the scenarios, waveform design methods and future prospects of terahertz communication perception integrated waveform were described and summarized.First of all, the scenarios and requirements of terahertz integrated waveform design were summarized, including perception-assisted communication scenarios, new business scenarios generated by perception, etc.Then, from the propagation characteristics of terahertz electromagnetic wave, the characteristics of terahertz devices, and the requirements of communication and perception on waveform, the characteristics of terahertz communication and perception integrated waveform were analyzed, and the research status of terahertz communication integrated waveform was summarized.Finally, the future research direction of terahertz synesthesia integrated waveform was pointed out.

Abstract Influenced by climate change and human activities, especially the completion and operation of cascade reservoirs in the middle and lower reaches of Jinsha River since 2012, new changes have taken place in the water and sediment characteristics of the Three Gorges Reservoir (TGR) in recent years. In this paper, a one-dimensional unsteady water and sediment mathematical model of the main and tributary rivers of the TGR is established, and the main calculation parameters of the model are calibrated with the measured water and sediment data from January 1, 2008 to December 31, 2017. In view of the different combinations of inflow water and sediment that may occur in the TGR under the condition of new water and sediment, the long-term changes of sediment erosion and deposition and the balance of reservoir deposition in the TGR are studied using the model. The results show that: (1) Under the new conditions of water and sediment, the amount of sediment in the TGR accounts for only 14.8% and 35.8% of that in 1956–1990 and 2003–2012, respectively; (2) The variation process of water level, discharge and sediment concentration of each station along the route calculated by the model is basically consistent with the measured results, and the calculated values of total deposition amount and deposition distribution are also basically consistent with the measured results. The verification results of the model are in accordance with the measured values; (3) Under the water-sediment conditions during 1961–1970 and 1991–2000, the model predicted the estimates of 320 and 430 years for the TGR to reach a sedimentation balance, respectively. Under the new water-sediment conditions, it takes 560 years at most and 450 years at least to reach the sedimentation balance for the TGR, and the corresponding condition is the typical year with less water-less sediment and more water-more sediment, respectively. The research results of this paper can provide a new reference for the long-term safe operation and operation optimization of the TGR.

With the rapid development of smart city,smart manufacturing and other industries,machine-type communication has gained much attention of academia and industry.The extent of function integration and intelligence of machine-type communication network has become increasingly high,which imposes more stringent requirements on network delay,reliability,number of connected devices,peak rate,security and scalability.Based on analyzing the change of performance requirements,key technology and corresponding network architecture of machine-type communication network in 4G,5G to future 6G system,the intelligent sensing-communication-computation integrated network architecture was proposed for 6G machine-type communication network.

More users suffering from depression turn to online forums to express their problems and seek help. In such forums, there is often a large volume of posts with sensitive content, indicating that the user has a risk of suicide and self-harm. Early detection of depression using appropriate deep learning models and social media data can prevent potential self-harm. However, existing depression detection models are not powerful enough to capture critical sentiment information from the large volume of posts published by each user, which makes the performance of these models not satisfying. To address this problem, we propose a hierarchical posts representations model named Multi-Gated LeakyReLU CNN (MGL-CNN) for identifying depressed individuals in online forums. The model consists of two parts: the first one is a post-level operation, which is used to learn the representation of each post of the user, and the second one is a user-level operation, which is used to obtain the overall representation of the user's emotional state. Besides, we propose another depression detection model by changing the number of gated units in the MGL-CNN, which is named Single-Gated LeakyReLU CNN (SGL-CNN). We show how to use our models to identify depressed users through a lot of posted content. Experimental results showed that our models performed better than the previous state-of-the-art models on the Reddit Self-reported Depression Diagnosis dataset, and also performed well on the Early Detection of Depression dataset.

Due to high mobility and flexibility, unmanned aerial vehicles (UAVs) have attracted significant attention from both academia and industry in wireless communications. Different types of UAVs are used for different wireless applications. However, there is a dilemma that no suitable approach is available to provide continuous available long-time wireless coverage from UAV-mounted base stations (UBSs) since rotary-wing UAVs require frequent energy replenishment while fixed-wing UAVs cannot hover at fixed locations. In this paper, the fixed-wing UBS is considered for providing continuous available wireless services to ground users with a novel dynamic resource allocation framework. In particular, the optimal resource allocations for both out-band and in-band wireless backhaul schemes are derived using convex optimization theory. Moreover, the sequential convex optimization method is used to obtain the energy-efficient trajectories. Finally, extensive simulation results are provided to verify the effectiveness of our proposed method. A comparison of out-band and in-band backhaul schemes is also provided.

Considerable interest has been shown in the coexistence between airborne radar and commercial communication systems in recent years. In particular, the integrated radar and communication system (IRCS) is promising for the airborne platforms like Unmanned Air Vehicles (UAVs). However, due to fast varying channels caused by high mobility, it is a great challenge for the fusion center to collect detection information within a given delay threshold through the air-to-ground (A2G) communication. Based on slowly varying components of the channel, i.e, target spectrum, power spectral densities of the signal dependent clutters, path loss and shadow fading, this paper considers the problem of power minimization for an IRCS and a base station (BS) coexisting in the same frequency band. The latency bound, latency violation probability (LVP), and channel capacity for the A2G communication to the fusion center are considered based on the effective capacity (EC) theory. The detection performance for radar and the rate requirement of BS user are also taken into account. The power allocation problem is non-convex and formulated to a monotonic optimization problem. Afterward, an efficient heuristic scheduling algorithm with acceptable computational complexity is proposed to solve the formulated problem. Then, the robust power allocation with channel estimation error is considered. Simulation results demonstrate the effectiveness of the proposed heuristic algorithm from the perspectives of the total transmit power, EC, and LVP of the IRCS.

Specific emitter identification (SEI) and automatic modulation classification (AMC) are generally two separate tasks in the field of radio monitoring. Both tasks have similarities in terms of their application scenarios, signal modeling, feature engineering, and classifier design. It is feasible and promising to integrate these two tasks, with the benefit of reducing the overall computational complexity and improving the classification accuracy of each task. In this paper, we propose a dual-task neural network named AMSCN that simultaneously classifies the modulation and the transmitter of the received signal. In the AMSCN, we first use a combination of DenseNet and Transformer as the backbone network to extract the distinguishable features; then, we design a mask-based dual-head classifier (MDHC) to reinforce the joint learning of the two tasks. To train the AMSCN, a multitask cross-entropy loss is proposed, which is the sum of the cross-entropy loss of the AMC and the cross-entropy loss of the SEI. Experimental results show that our method achieves performance gains for the SEI task with the aid of additional information from the AMC task. Compared with the traditional single-task model, our classification accuracy of the AMC is generally consistent with the state-of-the-art performance, while the classification accuracy of the SEI is improved from 52.2% to 54.7%, which demonstrates the effectiveness of the AMSCN.

Objectives: To evaluate the effect of in-hospital pulmonary rehabilitation (PR) on short-term pulmonary functional recovery in patients with COVID-19.Methods: Patients with COVID-19 (n = 123) were divided into two groups (PR group or Control group) according to recipient of pulmonary rehabilitation. Six-min walk distance (6MW), heart rate (HR), forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1), diffusing capacity of the lung for carbon monoxide (DLCO), and CT scanning were measured at the time of discharge, 1, 4, 12, and 24 weeks.Results: At week one, both PR group and Control group showed no significant changes in pulmonary function. At 4 and 12 weeks, 6MW, HR, FVC, FEV1, and DLCO improved significantly in both groups. However, the improvement in the PR group was greater than the Control group. Pulmonary function in the PR group returned to normal at 4 weeks [FVC (% predicted, PR vs. Control): 86.27 ± 9.14 vs. 78.87 ± 7.55; FEV1 (% predicted, PR vs. Control) 88.76 ± 6.22 vs. 78.96 ± 6.91; DLCO (% predicted, PR vs. Control): 87.27 ± 6.20 vs. 77.78 ± 5.85] compared to 12 weeks in the control group [FVC (% predicted, PR vs. Control): 90.61 ± 6.05 vs. 89.96 ± 4.05; FEV1 (% predicted, PR vs. Control) 94.06 ± 0.43 vs. 93.85 ± 5.61; DLCO (% predicted, PR vs. Control): 91.99 ± 8.73 vs. 88.57 ± 5.37]. Residual lesions on CT disappeared at week 4 in 49 patients in PR group and in 28 patients in control group (p = 0.0004).Conclusion: Pulmonary rehabilitation could accelerate the recovery of pulmonary function in patients with COVID-19.

Abstract Morchella importuna has been artificially cultivated, but stable production remains difficult because its mechanisms of fruiting body formation are unclear. To investigate the fruiting body formation mechanisms, we sequenced the transcriptomes of Morchella importuna at the mycelial and young fruiting body stages. Among the 12,561 differentially expressed genes (DEGs), 9215 were upregulated, and 3346 were downregulated. DEG enrichment analysis showed that these genes were enriched in the “generation of precursor metabolites and energy”, “carbohydrate catabolic process”, and “oxidoreductase activity” Gene Ontology (GO) functional categories. Enzyme activity assay results indicated that the activity levels of CAZymes (carbohydrate-active enzymes), oxidoreductases (SOD (superoxide dismutase), CAT (catalase)) and mitochondrial complex (complex I, II, III) proteins were significantly increased from the mycelial stage to the young fruiting body stage. In addition, the genes encoding CAZymes, mitochondrial proteins, oxidoreductases and heat shock proteins had higher expression levels in the young fruiting body stage than in the mycelial stage, and the qRT-PCR results showed similar trends to the RNA-Seq results. In summary, these results suggest that carbohydrate catabolism and energy metabolism are significantly enhanced in the young fruiting body stage and that growth environment temperature changes affect the formation of fruiting bodies.

In machine learning, training sample set management has an important impact on the performance of visual detection and tracking algorithms, as corrupted training samples degrade the tracking performance, especially in practical scenarios such as vehicular networks. However, how to evaluate and remove the corrupted training samples still remains a challenging topic. In this paper, we propose a novel scheme to remove the corrupted training samples in visual tracking, which will improve the tracking performance dramatically. In the proposed scheme, a novel training sample set management method based on the adaptive sample weight is presented. Specifically, similarity learning is first utilized to evaluate the quality of training samples with similarity score. Then, if the similarity score is below a certain threshold, the training sample is deemed as the corrupted one and is removed from the training sample set. The experimental results show that the proposed scheme obtains superior performances on visual tracking benchmarks and vehicular scenarios.

Unmanned aerial vehicles (UAVs), benefit by low cost, fast deployment, and large coverage, are widely applied as aerial relays in providing communication services for the areas with heavy traffic load or disasters. In this paper, the capacity of UAV relaying networks is studied, where the UAVs act as relays for the ground users. Two scenarios of UAVs distributed on an aerial 2-D plane and in a 3-D space are studied. The analysis reveals that in order to control the air-to-ground interference, the altitude of UAVs is at most in the same order to the distance between two adjacent UAVs. Besides, it is discovered that the pathloss exponent value α = 2.5 is a watershed for the capacity of UAV relaying networks, i.e., when α ≤ 2.5, the capacity of UAV relaying networks with the 2-D deployment of UAVs is greater than that with the 3-D deployment of UAVs. Otherwise, the capacity of UAV relaying networks with the 3-D deployment of UAVs is larger than that with the 2-D deployment of UAVs. Furthermore, the mobility of UAVs is studied, and it is verified that the mobility can orderly improve the capacity of UAV relaying networks. The theoretical analysis of this paper provides a guideline for the deployment of UAVs as aerial relays.

Taint-style vulnerabilities can damage the service provided by mobile seriously. The pattern-based method is a practical way to detect taint-style vulnerabilities. Most of the methods extract the vulnerability patterns from the code base, however, sometimes missing the vulnerability patterns and resulting in some vulnerabilities undiscovered. The security patches contain valuable information about the vulnerabilities. To compensate for the inherent incompleteness of pattern matching, in this paper, we propose an approach to infer patterns with the security information carried on the security patches. The taint-style vulnerability is described as a 3-tuples (Ssrc, Ssan, Ssink) here, which consist of sources(Ssrc), sanitization (Ssan), and sinks(Ssink). For each pair of vulnerable and patched programs, we extract the sanitizations from the changes between the vulnerable code and corresponding patches, infer the sinks with the impact analysis, and determine the sources through the backward traversal on the control flow graph. Finally, the complete-linkage clustering method is applied to the extracted triples to summary the patterns. We evaluate our method with open source projects. The results show our method is effective: 1) our method infers vulnerability patterns for taint-style vulnerabilities; 2) compared with the method inferring patterns from the code base, new patterns are discovered; and 3) the inferred patterns are applied to search the similar vulnerabilities successfully.

With plentiful spectrum resources, millimeter-wave (mmWave) band has been the forefront candidate to enable the requirements of lower latency and faster data rate for future automotive systems. However, the incorporation of mmWave directional transmission and diversely vehicular mobility triggers frequent beam realignment, thus largely increasing the beamforming overhead and leaving less time for data transmission. In this paper, an energy-angle domain access and transmission frame structure is investigated for mmWave vehicle-to-everything communications, which consists of two phases, initial access and data transmission. Considering the transmission interruption issue caused by blockage, we propose an energy-angle domain initial access scheme, by which the signals are labeled by different directions with multi-power level. Several performance metrics are subsequently obtained to evaluate the proposed scheme. Next, we formulate the access time-throughput tradeoff problem mathematically and prove that there indeed exists the optimal access time which yields the highest throughput for data transmission. Moreover, a binary-decision beam tracking scheme is designed to maintain the directional link connection in the data transmission phase. Numerical evaluations and simulations have been conducted to verify the correctness of our point of view and the superiority of our proposed methods.

The deep convolutional neural network has strong representative ability, which can learn latent information repeatedly from signal samples and improve the accuracy of automatic modulation classification (AMC). In this paper, a novel compressive convolutional neural network (CCNN) is proposed for AMC, where different constellation images, i.e., regular constellation images (RCs) and contrast enhanced grid constellation images (CGCs), are generated as network inputs from received signals. Moreover, a compressive loss constraint is proposed to train the CCNN, which aims at capturing high-dimensional features for modulation classification. Additionally, CCNN utilizes intra-class compactness and inter-class separability to enhance the classification and robustness performance for the different orders of modulations. The simulation results demonstrate that CCNN displays superior classification and robustness performance than existing AMC methods.

In machine-type communication (MTC) consisting high-mobility machines, such as vehicles, aircrafts, robots, etc., there is a strong demand for fast wireless networking. With radars extensively applied in these machines for environment awareness, radar assisted fast neighbor discovery (ND) for wireless ad hoc networks is proposed to realize fast wireless networking for MTC. Four neighbor discovery algorithms are designed according to the accuracy level of prior information provided by radar. Then, the performance of neighbor discovery algorithms is analyzed theoretically. Simulation results are further provided to compare neighbor discovery algorithms with and without the prior information from radar. The results demonstrate that the time consumption of neighbor discovery is greatly reduced with prior information of radar. Moreover, the time consumption with prior information from radar increases with the number of nodes. However, the increasing speed is much slower than the neighbor discovery without prior information from radar. This paper proves the advantages of radar assisted neighbor discovery, which may motivate the study of radar assisted wireless networking schemes in the future.

In this paper, we investigate the air-to-ground multi-traffic backhaul enabled by unmanned aerial vehicle (UAV) equipped with multiple radio access networks (RANs). Faced with the challenging traffic designation problem over diverse RANs, an efficient packet delivery scheme based on multi-priority batch arrival queuing theory is proposed to match various traffics with multiple RANs. Specifically, our contributions are three-fold: 1) The average queuing time of UAV-enabled multi-RAN access is theoretically derived according to multi-priority batch arrival queuing theory. 2) The priority factor and z-type utility function are well tailored by leveraging the derived queuing time. 3) A multiple traffic backhaul strategy is further proposed by exploiting the priority factor and z-type utility function to optimize the packet delivery, i.e., maximizing the delay utility via joint network selection and power allocation for diverse RANs. The simulation results show that the average delay of delay-sensitive packets is reduced by 15%, and the overall packet delivery quality is improved considerably.

In order to tackle the issue of multi-scale object detection, recent detectors usually adopt hierarchical feature pyramids which are generated by naive combinations of top-down features and lateral features. Considering the limited effective receptive fields of the methods for top-down features augmentation, the generated regions are only associated with the fixed areas of the coarser features. Meanwhile, noisy features are introduced by irrelevant regions inevitably since the finer features in relation to rigid coarser regions. Thus, the pyramidal features with strong semantics are difficult to be obtained via simply enlarging the top-down features. In this paper, we present the Aggregated Residual Dilation based Feature Pyramid Network (ARDFPN) to exploit the inherent correlation of regions in feature pyramid. The network is designed by stacking a building block that aggregates a set of dilated convolutions with the same topology. We show that carefully adding additional transformation stages into feature pyramid enables a potential way for further multi-scale feature generation. As an intuitive extension of Feature Pyramid Network (FPN), we conduct an exhaustive study to evaluate the model performance by replacing FPN with the proposed ARDFPN in both object detection and instance segmentation tasks. With Residual network in Faster R-CNN and Mask R-CNN framework, ARDFPN outperforms the prevalent detection module - FPN on the challenging COCO dataset without bells and whistles. In particular, ARDFPN exhibits a superior performance, especially for the small and middle objects.

A number of methods have been proposed for face reconstruction from single/multiple image(s). However, it is still a challenge to do reconstruction for limited number of wild images, in which there exists complex different imaging conditions, various face appearance, and limited number of high-quality images. And most current mesh model based methods cannot generate high-quality face model because of the local mapping deviation in geometric optics and distortion error brought by discrete differential operation. In this paper, accurate geometrical consistency modeling on B-spline parameter domain is proposed to reconstruct high-quality face surface from the various images. The modeling is completely consistent with the law of geometric optics, and B-spline reduces the distortion during surface deformation. In our method, 0th- and 1st-order consistency of stereo are formulated based on low-rank texture structures and local normals, respectively, to approach the pinpoint geometric modeling for face reconstruction. A practical solution combining the two consistency as well as an iterative algorithm is proposed to optimize high-detailed B-spline face effectively. Extensive empirical evaluations on synthetic data and unconstrained data are conducted, and the experimental results demonstrate the effectiveness of our method on challenging scenario, e.g., limited number of images with different head poses, illuminations, and expressions.

With autonomous driving developing in a booming stage, accurate object detection in complex scenarios attract wide attention to ensure the safety of autonomous driving. Millimeter wave (mmWave) radar and vision fusion is a mainstream solution for accurate obstacle detection. This article presents a detailed survey on mmWave radar and vision fusion based obstacle detection methods. First, we introduce the tasks, evaluation criteria, and datasets of object detection for autonomous driving. The process of mmWave radar and vision fusion is then divided into three parts: sensor deployment, sensor calibration, and sensor fusion, which are reviewed comprehensively. Specifically, we classify the fusion methods into data level, decision level, and feature level fusion methods. In addition, we introduce three-dimensional(3D) object detection, the fusion of lidar and vision in autonomous driving and multimodal information fusion, which are promising for the future. Finally, we summarize this article.

Abstract Millimeter wave (mmWave) communications is a prospective candidate technology for multi-gigabit rates multimedia applications. To combat the severe propagation attenuation of mmWave, the high gain directional antenna is commonly employed at the nodes. Moreover, exploiting multiple radios over multiple channels is also a promising technology to improve the throughput and delay performance of mmWave communications. In this paper, considering the signal to interference plus noise ratio (SINR) constraint, we develop a multi-path multi-hop multi-radio multi-channel (MPMH-MRMC) concurrent transmission scheduling algorithm to fully exploit spatial reuse in mmWave wireless personal area networks (WPANs). The problem of MPMH-MRMC scheduling is formulated as a mixed-integer linear programming (MILP) to clear all the flows within a minimum number of time slots, which is generally NP-hard. We further propose a heuristic MPMH-MRMC scheme with low computational complexity to solve the problem. Finally, through extensive simulations, we demonstrate that MPMH-MRMC can significantly improve the network performance in terms of network throughput and transmission delay under various traffic patterns.

The unmanned aerial vehicle (UAV) networks have wide civil applications. However, due to the 3D deployment and high mobility of UAVs, the neighbor discovery for UAV networks faces great challenges. In this paper, we have designed a two-way handshaking neighbor discovery scheme for UAV networks. A scanning path for 3D UAV networks is proposed. In order to reduce the overhead of neighbor discovery, each UAV varies among the states of transmit, receive, and sleep with certain probabilities. The sleep probability is closely related to the probability of topology change of UAV networks. The optimal sleep probability is derived according to the mobility model of UAVs. The efficiency of the proposed neighbor discovery scheme is analyzed via the Markov process. The optimal transmission probability of UAVs is derived to improve the efficiency of neighbor discovery. Besides, the impact of various parameters on the efficiency of neighbor discovery is analyzed by simulation results. This paper has considered the deployment and mobility of UAVs in the design of neighbor discovery scheme, which provides an insight in the design of neighbor discovery schemes for UAV networks.

As the process of identifying the modulation format of the received signal, automatic modulation classification (AMC) has various applications in spectrum monitoring and signal interception. In this paper, we propose a dictionary learning-based AMC framework, where a dictionary is trained using signals with known modulation formats and the modulation format of the target signal is determined by its sparse representation on the dictionary. We also design a dictionary learning algorithm called block coordinate descent dictionary learning (BCDL). Furthermore, we prove the convergence of BCDL and quantify its convergence speed in a closed form. Simulation results show that our proposed AMC scheme offers superior performance than the existing methods with low complexity.

For business processes based on micro service architecture in an enterprise, failures often occur because of modified business rules or goals, change of service availability, and dynamical running environment. Based on dynamical replanning technologies, these failures can be repaired at runtime. However, semantic conflicts among services from different providers can greatly decrease response efficiency and response success rate of a business goal. In this paper, we propose a novel service runtime self-adaptation framework to decrease response time and raise success rate. Distributed dynamic description logic is utilized to eliminate semantic conflicts among services and provide basic models for carrying out planning among services. Considering inputs, outputs, preconditions, and effects properties of services, local and global planning algorithms based on artificial intelligence graph planning are designed. Local planning can rapidly search a service-based path only including services from a provider, and global planning can try to explore a path including services from multiple providers. Based on these two algorithms, local and global replanning strategies are designed to handle runtime exceptions at service level and path level. We implement a prototype system by means of workflow engine Activiti and business process language BPMN2.0. Experiments show that compared with previous works, our framework can guarantee higher efficiency and success rate.

GPS trajectory data plays a critical role for the intelligent transportation. Due to many factors like measure error, sampling error and battery power-saving requirement, the directly obtained trajectories will inaccurately align to the digital map. This requires an online map matching algorithm with high precision, low latency, and energy consumption saving. However, the existing approaches need to make some trade off among these criterions. Hence, based on the hidden Markov model, this paper proposes an adaptive online map matching algorithm to improve the performances in these perspectives at the same time: 1) the probabilistic method integrating the geometric information and topological information is developed to improve the accuracy; 2) the adaptive sampling frequency method is proposed to reduce the energy consumption; and 3) the adaptive sliding window method is presented to reduce the output delay. The experiments demonstrate that our approaches can not only improve the matching precision, but also reduce the latency and energy consumption simultaneously.

For object detection in computer vision, detection models trained by high-resolution images often fail to recognize or localize objects on low-resolution images. To tackle this problem, we propose a fully convolutional network named residual super-resolution single shot network (RSRSSN). RSRSSN consists of two sub-networks, super-resolution sub-network and detection sub-network. The super-resolution subnetwork in RSRSSN is achieved by stacking of identity residual blocks while the detection sub-network adopts the single shot multibox detector (SSD). Based on multi-task learning, we design a novel objective function called feature maps multibox loss to enforce low-resolution images to produce similar feature maps with their corresponding high-resolution ones. This information sharing mechanism is proved to be critical for solving the resolution mismatch problem in the experiments. A two-step training scheme is also proposed to train the RSRSSN in an end-to-end manner. Without any data augmentation, RSRSSN outperforms the SSD on both down-sampled PASCAL VOC and MS COCO in real-time object detection.

As the spectrum environment becomes increasingly crowded and complicated, primary users may be interfered by secondary users and other illegal users. Automatic modulation classification (AMC) of a single source cannot recognize the overlapped sources. Consequently, the AMC of overlapped sources attracts much attention. In this paper, we propose a genetic programming-based modulation classification method for overlapped sources (GPOS). The proposed GPOS consists of two stages, the training stage, and the classification stage. In the training stage, multi-gene genetic programming (MGP)-based feature engineering transforms sample estimates of cumulants into highly discriminative MGP-features iteratively, until optimal MGP-features (OMGP-features) are obtained, where the structural risk minimization principle (SRMP) is employed to evaluate the classification performance of MGP-features and train the classifier. Moreover, a self-adaptive genetic operation is designed to accelerate the feature engineering process. In the classification stage, the classification decision is made by the trained classifier using the OMGP-features. Through simulation results, we demonstrate that the proposed scheme outperforms other existing methods in terms of classification performance and robustness in case of varying power ratios and fading channel.

Mobile edge computing (MEC) technology migrates the cloud computing platform to the edge of the mobile access network, which reduces the end-to-end delay of mobile service delivery and improves the user experience. However, since the number of the deployed small cell base stations (BSs) is larger than the macro BSs, automatic deployment can be used to control deployment costs. In this paper, we study the adaptive boundary algorithm of indoor small cell BSs and propose a power optimization scheme in self-organizing MEC-based heterogeneous small cell networks. According to the actual environment, the indoor small cell BS can be deployed quickly and flexibly. And the coverage of the small cell BS can be optimized by automatically adjusting the power parameters. Considering the coverage and spectral efficiency of the macro BS and the small cell BS, the performance indicators of the optimization program and results evaluation is proposed. Finally, the simulation results show that the proposed algorithm and scheme can control the deployment cost and improve the optimization coverage of macro BSs and small cell BSs under the control cost.

Abstract In the present study, we aimed to assess the effect of hydrogen-rich water (HRW) on the physicochemical characteristics and antioxidant capacity of Hypsizygus marmoreus during 12 days of postharvest storage at 4 °C. Different concentrations of HRW (25, 50 and 100%) were tested, and our data showed that 25% HRW treatment had the most significant effect on preservation of nutrients in H. marmoreus compared with the control group. In addition, 25% HRW treatment significantly reduced the relative electrolyte leakage rate and malonaldehyde (MDA) content (P

Reservoir construction may modify the downstream flow and sediment transport, and correspondingly result in adjustments in morphodynamics of a river, especially riverbed instability. Based on hydrological datasets from 2003 to 2019 during the post-dam period using the topographic data of 57 fixed cross-sections in the Wuhan reach of the Yangtze River, we calculated the indexes representing the channel stability. Moreover, considering the effects of flow, sediment concentration, grain size of sediment, and water depth, we propose a method for calculating the equilibrium values of cumulative erosion and the lateral migrate intensity of thalweg in this paper, and the method combines with the delayed response model (DRM) to comprehensively analyze the variations in the longitudinal and lateral stabilities of the riverbed. The results revealed that the channel has been obviously eroded in the downstream reach, resulting in a 76% decrease in sediment discharge after the impoundment of the Three Gorges Reservoir (TGR). Specifically, in the past 17 years, the cumulative erosion in the Wuhan reach of the Yangtze River reached 1.72 m, while the bankfull depth increased by 1.87 m. The lateral migrate intensity of thalweg increased in response to Coriolis force, with an increase of 22.3%. Taken together, the results show that the proposed formula can effectively simulate the variation process of channel stability, and it also quantifies the extent of the influence weight of interannual flow and sediment regimes. The morphodynamics adjustments in the channel stability of our studied reach were closely related to the previous five-year flow and sediment regimes, implying that channel evolution may lag behind the changes in flow and sediment discharges.

Due to the increasing number of vehicles equipped with millimeter wave (mmWave) radars, interference among automotive radars is becoming a major issue. This paper explores the automotive radar interference in both two-lane and multi-lane scenarios using stochastic geometry. We derive closed-form expressions for mean and variance of interference power considering directional antenna with constant and Gaussian decaying gains. In view of the sensitivity of mmWave radar signals to the blockages, we propose a blockage model including partially and completely blocking, and then calculate the effective number of the interferers. By means of modeling randomness for interferers and blockages as Poisson point process, we characterize the statistics of radar interference under different conditions. We further utilize the interference characterization to estimate the successful ranging probability of automotive radars. These theoretical analyses are verified by using Monte Carlo simulations. The results show that the increasing interfering density and ranging distance largely degrade the radar detection performance, whereas the interference levels decrease as blockage intensity increases.

Abstract Background Biological environment is uncertain and its dynamic is similar to the multiagent environment, thus the research results of the multiagent system area can provide valuable insights to the understanding of biology and are of great significance for the study of biology. Learning in a multiagent environment is highly dynamic since the environment is not stationary anymore and each agent’s behavior changes adaptively in response to other coexisting learners, and vice versa. The dynamics becomes more unpredictable when we move from fixed-agent interaction environments to multiagent social learning framework. Analytical understanding of the underlying dynamics is important and challenging. Results In this work, we present a social learning framework with homogeneous learners (e.g., Policy Hill Climbing (PHC) learners), and model the behavior of players in the social learning framework as a hybrid dynamical system. By analyzing the dynamical system, we obtain some conditions about convergence or non-convergence. We experimentally verify the predictive power of our model using a number of representative games. Experimental results confirm the theoretical analysis. Conclusion Under multiagent social learning framework, we modeled the behavior of agent in biologic environment, and theoretically analyzed the dynamics of the model. We present some sufficient conditions about convergence or non-convergence and prove them theoretically. It can be used to predict the convergence of the system.

Abstract In plants, hydrogen gas (H2) enhances tolerance to several abiotic stresses, including salinity and heavy metals. However, the effect of H2 on fungal growth under different stresses remains largely unclear. In this study, hydrogen-rich water (HRW) was employed to characterize physiological roles and molecular mechanisms of H2 in the alleviation of three different stresses in basidiomycete Hypsizygus marmoreus. Our results showed that HRW treatment, of which the H2 concentration was 0.8 mM, significantly reduced the toxicities of CdCl2, NaCl and H2O2, leading to significantly improved mycelial growth and biomass. These beneficial effects could be attributed to a significantly decreased formation of malondialdehyde (MDA). Besides, HRW treatment significantly increased the activities of antioxidants (SOD, CAT and GR) as well as the gene expressions of these antioxidants (SOD, CAT, and GR) at the mRNA level. In vivo detection of reactive oxygen species (ROS), including H2O2 and O2 −, as well as lipid peroxidation provided further evidence that HRW could significantly improve tolerances of CdCl2, NaCl and H2O2. Furthermore, pyruvate kinase was activated in the mycelia treated with HRW, along with its induced gene expression, suggesting that HRW treatment enhanced the glucose metabolism. Taken together, our findings suggested that the usage of HRW could be an effective approach for contaminant detoxification in H. marmoreus, which was similar with the effects of HRW in plants, and such effects could be also beneficial in entire agricultural system.

The diversified development and popularization of the mobile social network platform has made the demand of data transmission for mobile users show an explosive growth trend,and put forward new challenges to the lack of spectrum resources and the management of high load base stations. To solve such problems, the distributed caching system was introduced into the wireless collaboration network by allowing users to cache popular content items cooperatively, for effective content sharing among users with low energy consumption,high reliability and low latency,while offloading base station and backhaul data traffic. Firstly, the challenges and problems for wireless resource allocation in wireless distributed caching system were analyzed.Graph and matching theory based on methodologies were discussed to solve the resource management problems in terms of energy efficiency,spectral efficiency and secure transmission respectively.In addition,the feasibility of mining and using social information to improve the efficiency of wireless resources was discussed and analyzed, which provided a brief outlook on future interdisciplinary research between social science and wireless network.

As more and more ontologies are defined with different terms, ontology matching plays a crucial role in addressing the semantic heterogeneity problem in many different disciplines. Many efforts have been made to discover correspondences among terms in different ontologies. Most studies directly match two ontologies by utilizing terminological and structural methods that rely on ontologies themselves only. However, the decentralized characteristic of ontologies raises the uncertainty in ontology matching. To address this problem, we propose a four-stage ontology matching framework (FOMF) to enhance ontology matching performance. It is built upon the commonly accepted claim that an external comprehensive knowledge base can be used as a semantic bridge between domain ontologies for ontology matching. First, FOMF semantically maps domain ontologies to a knowledge base and then produces different types of alignments, including equivalence, subclass, sameas, and instance alignments. Similarities between two domain ontologies are next employed to enhance the equivalence and sameas alignments discovery. Finally, based on acquired alignments, inferred alignments are deduced to guarantee the completeness of matching results. Our experimental results show the superiority of the proposed method over the existing ones.