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Modifying network results is the most intuitive way to inject domain knowledge into network detection algorithms to improve their performance. While advances in computation scalability have made detecting large‐scale networks possible, the human ability to modify such networks has not scaled accordingly, resulting in a huge 'interaction gap'. Most existing works only support navigating and modifying edges one by one in a graph visualization, which causes a significant interaction burden when faced with large‐scale networks. In this work, we propose a novel graph pattern mining algorithm based on the minimum description length (MDL) principle to partition and summarize multi‐feature and isomorphic sub‐graph matches. The mined sub‐graph patterns can be utilized as mediums for modifying large‐scale networks. Combining two traditional approaches, we introduce a new coarse‐middle‐fine graph modification paradigm (i.e. query graph‐based modification →$\rightarrow$ sub‐graph pattern‐based modification →$\rightarrow$ raw edge‐based modification). We further present a graph modification system that supports the graph modification paradigm for improving the scalability of modifying detected large‐scale networks. We evaluate the performance of our graph pattern mining algorithm through an experimental study, demonstrate the usefulness of our system through a case study, and illustrate the efficiency of our graph modification paradigm through a user study. [ABSTRACT FROM AUTHOR]

This article focuses on the fixed-time stability of a class of nonlinear systems with uncertain disturbances and time-varying delays. Different from other papers, the second-order sliding mode control algorithm (SOSMCA) is developed in this article to study the fixed-time stability of nonlinear systems for the first time. Additionally, the novel sliding mode surface (SMC) and second-order SMC are presented. And the stability and reachability of the sliding-mode dynamics under a faster settling time are demonstrated. Finally, the applicability and validity of the obtained SOSMCA are demonstrated by a simulation example with the F-404 aircraft engine model. [ABSTRACT FROM AUTHOR]

For systems with uncertain parameters, it is very important to find a controller that can satisfy the preset robustness. The traditional method often ignores the influence of parameter coupling on the set boundary system when substituting the original system with the boundary system group, resulting in the calculated controller value region being too conservative. In this article, noise information is introduced based on affine algorithm to describe uncertain system parameters. Then, based on Kharitonov theorem, a new grouping method for boundary systems is proposed. This method takes the parameter coupling information into account when determining the boundary system, and avoids the problem of interval conservation. On this basis, a virtual phase margin tester is introduced to ensure that the obtained controller parameter range can make the system meet the specific robustness requirements. The results obtained in this article are general and strictly proved. Finally, examples are provided to illustrate the design process and verify the feasibility and efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

Detecting and recognizing pests are paramount for ensuring the healthy growth of crops, maintaining ecological balance, and enhancing food production. With the advancement of artificial intelligence technologies, traditional pest detection and recognition algorithms based on manually selected pest features have gradually been substituted by deep learning-based algorithms. In this review paper, we first introduce the primary neural network architectures and evaluation metrics in the field of pest detection and pest recognition. Subsequently, we summarize widely used public datasets for pest detection and recognition. Following this, we present various pest detection and recognition algorithms proposed in recent years, providing detailed descriptions of each algorithm and their respective performance metrics. Finally, we outline the challenges that current deep learning-based pest detection and recognition algorithms encounter and propose future research directions for related algorithms. [ABSTRACT FROM AUTHOR]

Appropriate needle manipulation to avoid abrupt deformation of fragile vessels is a critical determinant of the success of microvascular anastomosis. However, no study has yet evaluated the area changes in surgical objects using surgical videos. The present study therefore aimed to develop a deep learning-based semantic segmentation algorithm to assess the area change of vessels during microvascular anastomosis for objective surgical skill assessment with regard to the "respect for tissue." The semantic segmentation algorithm was trained based on a ResNet-50 network using microvascular end-to-side anastomosis training videos with artificial blood vessels. Using the created model, video parameters during a single stitch completion task, including the coefficient of variation of vessel area (CV-VA), relative change in vessel area per unit time (ΔVA), and the number of tissue deformation errors (TDE), as defined by a ΔVA threshold, were compared between expert and novice surgeons. A high validation accuracy (99.1%) and Intersection over Union (0.93) were obtained for the auto-segmentation model. During the single-stitch task, the expert surgeons displayed lower values of CV-VA (p < 0.05) and ΔVA (p < 0.05). Additionally, experts committed significantly fewer TDEs than novices (p < 0.05), and completed the task in a shorter time (p < 0.01). Receiver operating curve analyses indicated relatively strong discriminative capabilities for each video parameter and task completion time, while the combined use of the task completion time and video parameters demonstrated complete discriminative power between experts and novices. In conclusion, the assessment of changes in the vessel area during microvascular anastomosis using a deep learning-based semantic segmentation algorithm is presented as a novel concept for evaluating microsurgical performance. This will be useful in future computer-aided devices to enhance surgical education and patient safety. [ABSTRACT FROM AUTHOR]

Background: Lumbar disc herniation is a common degenerative lumbar disease with an increasing incidence. Percutaneous endoscopic lumbar discectomy can treat lumbar disc herniation safely and effectively with a minimally invasive procedure. However, the learning curve of this technology is steep, which means that initial learners are often not sufficiently proficient in endoscopic operations, which can easily lead to iatrogenic damage. At present, the application of computer deep learning technology to clinical diagnosis, treatment, and surgical navigation has achieved satisfactory results. Purpose: The objective of our team is to develop a multi-element identification system for the visual field of endoscopic spine surgery using deep learning algorithms and to evaluate the feasibility of this system. Method: We established an image database by collecting surgical videos of 48 patients diagnosed with lumbar disc herniation, which was labeled by two spinal surgeons. We selected 6000 images of the visual field of percutaneous endoscopic spine surgery (including various tissue structures and surgical instruments), divided into the training data, validation data, and test data according to 2:1:2. We developed convolutional neural network models based on instance segmentation—Solov2, CondInst, Mask R-CNN and Yolact, and set the four network model backbone as ResNet101 and ResNet50 respectively. Mean average precision (mAP) and frames per second (FPS) were used to measure the performance of each model for classification, localization and recognition in real time, and AP (average) is used to evaluate how easily an element is detected by neural networks based on computer deep learning. Result: Comprehensively comparing mAP and FSP of each model for bounding box test and segmentation task for the test set of images, we found that Solov2 (ResNet101) (mAP = 73.5%, FPS = 28.9), Mask R-CNN (ResNet101) (mAP = 72.8%, FPS = 28.5) models are the most stable, with higher precision and faster image processing speed. Combining the average precision of the elements in the bounding box test and segmentation tasks in each network, the AP(average) was highest for tool 3 (bbox-0.85, segm-0.89) and lowest for tool 5 (bbox-0.63, segm-0.72) in the instrumentation, whereas in the anatomical tissue elements, the fibrosus annulus (bbox-0.68, segm-0.69) and ligamentum flavum (bbox-0.65, segm-0.62) had higher AP(average),while extra-dural fat (bbox-0.42, segm-0.44) was lowest. Conclusion: Our team has developed a multi-element identification system for the visual field of percutaneous endoscopic spine surgery adapted to the interlaminar and foraminal approaches, which can identify and track anatomical tissue (nerve, ligamentum flavum, nucleus pulposus, etc.) and surgical instruments (endoscopic forceps, an high-speed diamond burr, etc.), which can be used in the future as a virtual educational tool or applied to the intraoperative real-time assistance system for spinal endoscopic operation. [ABSTRACT FROM AUTHOR]

Orthogonal frequency division multiplexing (OFDM) in 5G has many advantages; however, one of the disadvantages is that the superposition of a large number of subcarriers leads to a high peak-to-average power ratio (PAPR) of the transmit signal. A high PAPR results in high-power amplifier distortion and performance degradation. The partial transmit sequence (PTS) algorithm is commonly used for PAPR reduction. It enumerates all combinations of phase factors, weighs the signal using each phase factor combination, and finds the set of phase factors that minimizes the PAPR value of the OFDM signal. The advantage of the PTS is that it determines the optimal solution through enumeration; however, its major drawback is the higher complexity caused by the use of enumeration. Some studies have introduced the discrete particle swarm optimization (DPSO) algorithm instead of enumeration to determine the optimal solution of the PTS algorithm. As an excellent optimization method, the DPSO algorithm represents each individual as a solution during the optimization. Through iterative updates of the initial population, individuals in the population continuously move closer to the optimal solution. This approach significantly reduces complexity compared with the exhaustive enumeration used in the traditional PTS algorithm. However, the disadvantage of the general DPSO algorithm is that it can result in premature and early convergence, which leads to degradation of the PAPR reduction performance. In this study, we propose an improved method based on the general DPSO-based PTS algorithm, and the improved algorithm MDPSO-PTS adopts dynamic time-varying learning factors, which can find the optimal combination of phase factors more efficiently. The MDPSO-PTS algorithm expands the search space when seeking the optimal combination of phase factors. This avoids the drawback of premature convergence commonly observed in general DPSO-PTS algorithms, preventing early consideration of local optima as global optima. A comparative simulation of the improved MDPSO-PTS algorithm with the general DPSO-PTS algorithm shows that the improved algorithm has stronger PAPR reduction, whereas the complexity remains basically unchanged. A comparative simulation with the traditional PTS algorithm shows a significant reduction in complexity, with only a slight, acceptable loss of reduction performance. [ABSTRACT FROM AUTHOR]

The location of Low-Altitude Flight Service Station (LAFSS) is a comprehensive decision work, and it is also a multi-objective optimization problem (MOOP) with constraints. As a swarm intelligence search algorithm for solving constrained MOOP, the Immune Algorithm (IA) retains the excellent characteristics of genetic algorithm. Using some characteristic information or knowledge of the problem selectively and purposefully, the degradation phenomenon in the optimization process can be suppressed and the global optimum can be achieved. However, due to the large range involved in the low-altitude transition flight, the geographical characteristics, economic level and service requirements among the candidate stations in the corridor are quite different, and the operational safety and service efficiency are interrelated and conflict with each other. And all objectives cannot be optimal. Therefore, this article proposes a Modified Immune Algorithm (MIA) with two-layer response to solve the constrained multi-objective location mathematical model of LAFSS. The first layer uses the demand track as the cell membrane positioning pattern recognition service response distance to trigger the innate immunity to achieve the basic requirements of security service coverage. In the second layer, the expansion and upgrading of adjacent candidate sites are compared to the pathogen's effector, and the adaptive immunity is directly or indirectly triggered again through the cloning, mutation and reproduction between candidate sites to realize the multiobjective equilibrium of the scheme. Taking 486,000 km2 of Sichuan Province as an example, MIA for LAFSS is simulated by the MATLAB platform. Based on the Spring open source application framework of Java platform, the cesiumjs map data is called through easyui, and the visualization of site selection scheme is presented with the terrain data of Map World as the background. The experimental results show that, compared with dynamic programming and ordinary immunization, the immune trigger mode of double response and the improved algorithm of operation parameter combination designed by the Taguchi experiment, the total economic cost of location selection is reduced by 26.4%, the service response time is reduced by 25%, the repeat coverage rate is reduced by 29.5% and the effective service area is increased by 17.5%. The security risk, service efficiency and location cost are balanced. The present work is to provide an effective location method for the layout number and location of local transfer flight service stations. For complex scenes with larger scale of low-altitude flight supply and demand and larger terrain changes in the region, the above research methods can be used to effectively split and reduce the dimension. [ABSTRACT FROM AUTHOR]

This paper studies the problem of intelligence optimization, a fundamental problem in analyzing the optimal solution in a wide spectrum of applications such as transportation and wireless sensor network (WSN). To achieve better optimization capability, we propose a multigroup Multistrategy Compact Sine Cosine Algorithm (MCSCA) by using the compact strategy and grouping strategy, which makes the initialized randomly generated value no longer an individual in the population and avoids falling into the local optimum. New evolution formulas are proposed for the intergroup communication strategy. Performance studies on the CEC2013 benchmark demonstrate the effectiveness of our new approach regarding convergence speed and accuracy. Finally, we apply MCSCA to solve the dispatch system of public transit vehicles. Experimental results show that MCSCA can achieve better optimization., Author(s): Minghui Zhu [1]; Shu-Chuan Chu [1]; Qingyong Yang [1]; Wei Li [2]; Jeng-Shyang Pan (corresponding author) [1] 1. Introduction The optimization problem is the problem of finding the optimal [...]

Satellite range scheduling, a multi-constrained combinatorial optimization problem, is crucial to guaranteeing the normal operation and application of onboard satellites. Traditional methods are dedicated to finding one optimal schedule, having ignored the problem may process multiple high-quality schedules. To provide a set of alternative schedules while maintaining the solution quality, we propose a co-evolutionary algorithm with elite archive strategy (COEAS) in this article. In COEAS, two populations are evolved to solve the original and relaxed problem in terms of schedule quality and diversity, respectively. During the evolution, the populations maintain a weak cooperation and only share the information in offspring combination phase. Further, an elite archive strategy is derived to identify and preserve potential stagnated and optimal individuals. In this strategy, the promising individuals would further participate in parent mating and offspring replacement for the dual purpose of maintaining potential optima recovery and fine-tuning the population. The experimental results show that the proposed algorithm is better than comparison algorithms in terms of efficacy (obtaining higher quality schedule), diversity (locating more optimal schedules) and flexibility (providing better alternatives). [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) in deep learning have input pixel limitations, which leads to lost information regarding microcalcification when mammography images are compressed. Segmenting images into patches retains the original resolution when inputting them into the CNN and allows for identifying the location of calcification. This study aimed to develop a mammographic calcification detection method using deep learning by classifying the presence of calcification in the breast. Using publicly available data, 212 mammograms from 81 women were segmented into 224 × 224-pixel patches, producing 15,049 patches. These were visually classified for calcification and divided into five subsets for training and evaluation using fivefold cross-validation, ensuring image consistency. ResNet18, ResNet50, and ResNet101 were used for training, each of which created a two-class calcification classifier. The ResNet18 classifier achieved an overall accuracy of 96.0%, mammogram accuracy of 95.8%, an area under the curve (AUC) of 0.96, and a processing time of 0.07 s. The results of ResNet50 indicated 96.4% overall accuracy, 96.3% mammogram accuracy, an AUC of 0.96, and a processing time of 0.14 s. The results of ResNet101 indicated 96.3% overall accuracy, 96.1% mammogram accuracy, an AUC of 0.96, and a processing time of 0.20 s. This developed method offers quick, accurate calcification classification and efficient visualization of calcification locations. [ABSTRACT FROM AUTHOR]

Quantum key distribution (QKD) is an encrypted communication technique based on the principles of quantum mechanics that ensures communication security by exploiting the properties of quantum states. Currently, the transmission efficiency of the QKD system is low. Trusted relay technology is used to solve this problem and achieve long-distance transmission. However, trusted relaying alone cannot decrypt the issues of poor link stability and the low utilization of key resources. To further optimize the system performance, we propose a dynamic routing algorithm. One of the improvement schemes includes the following: firstly, an adjustable-size quantum key pool (QKP) is designed, which can dynamically adjust the size of the refreshing pool according to the actual demand. Secondly, the utilization of key resources is improved by using the residual quantum key model to dynamically obtain the remaining key amount in the QKP and set the key amount threshold. We calculate the link-blocking probability and track the blocking intensity and blocking entry by combining the Poisson process, thus realizing the evaluation of the link stability. Finally, the number of remaining keys in the QKP and the link-blocking probability combine with the random wandering model as the basis of the route selection for the QKD dynamic routing algorithm to achieve efficient key path selection. We validated the algorithm by comparing it with other algorithms on the Mininet simulation platform, and the algorithm proved to have a better performance in terms of congestion avoidance, delay reduction, and improved QKD efficiency. This scheme provides a novel and efficient way to solve the problems in existing QKD systems. It effectively improves the transmission efficiency and strengthens the system's security by dynamically obtaining the critical volume, accurately evaluating the link state, and selecting the optimal critical path. [ABSTRACT FROM AUTHOR]

Blockchain technologies can enable secure computing environments among mistrusting parties. Permissioned blockchains are particularly enlightened by companies, enterprises, and government agencies due to their efficiency, customizability, and governance-friendly features. Obviously, seamlessly fusing blockchain and cloud computing can significantly benefit permissioned blockchains; nevertheless, most blockchains implemented on clouds are originally designed for loosely-coupled networks where nodes communicate asynchronously, failing to take advantages of the closely-coupled nature of cloud servers. In this paper, we propose an innovative cloud-oriented blockchain – CloudChain, which is a modularized three-layer system composed of the network layer, consensus layer, and blockchain layer. CloudChain is based on a shared-memory model where nodes communicate synchronously by direct memory accesses. We realize the shared-memory model with the Remote Direct Memory Access technology, based on which we propose a shared-memory consensus algorithm to ensure presistence and liveness, the two crucial blockchain security properties countering Byzantine nodes. We also implement a CloudChain prototype based on a RoCEv2-based testbed to experimentally validate our design, and the results verify the feasibility and efficiency of CloudChain. [ABSTRACT FROM AUTHOR]

Performance of direction of arrival (DOA) estimation is one of the most important issues in array signal processing. Subspace-based algorithms provide a good compromise between the estimation accuracy and computational complexity. However, these methods are exposed to performance breakdown at low SNR scenarios. A major reason for such performance breakdown is the subspace swap phenomenon (intersubspace leakage). In this article, we elaborate on a novel modified signal subspace model through exploiting a supervised index for the estimation of DOA. With the developed model we refine the signal subspace so as to enhance the performance of the DOA estimation. In the proposed scheme, we define a fuzzy similarity matrix for the eigenvalues of the array output correlation matrix to capture the distribution of the eigenvalues. Then, we build up a transformation matrix between the fuzzy similarity matrix and the eigenspace of the correlation matrix, and construct a nonlinear transformation function to adjust the fuzzy similarity matrix. Subsequently, we define a supervised evaluation index named signal subspace reconstruction error for DOA estimation and construct a cost function of the signal subspace to develop a supervised model for the signal subspace. The signal subspace can be modified through adjusting the parameter in the nonlinear transformation function and optimizing the abovementioned cost function. Finally, the performance of DOA estimation can be enhanced with the modified signal subspace. The main characteristic of the proposed model is circularly applied feedback of the estimated DOA for refining the estimated subspace. It is a closed loop and supervised method not reported before. This article opens a specific way for improving the performance of the DOA estimation in array signal processing by a supervised index. However, the proposed method is still unsatisfying in some scopes of signal-to-noise ratio. We believe that exploiting a validity index for DOA estimation in array signal processing is still a general and interesting problem. [ABSTRACT FROM AUTHOR]

With the improvement of our modernization level and the development of urbanization, the phenomenon of large crowds gathering is very common, which brings great hidden danger to public safety. Crowd counting plays a very important role in the field of intelligent video surveillance. It can estimate the total number of people according to the image and provide real-time warning, which can effectively avoid the occurrence of safety accidents. In this paper, in order to build an efficient lightweight crowd density estimation model, we proposed a dilated and depthwise separable convolution network. In the network, we build a dilated and depthwise separable convolution module, which improves the receptive field of the convolution kernel without increasing network parameters. Meanwhile, sparse convolutional kernels are used to extract sparse features, and compact convolutional kernels are used to extract more dense features, thus improving the efficiency of the convolutional kernels. Through experimental comparison on SHT Part A data set, the accuracy of the proposed method is 2.6% higher than that of MCNN, and the parameters are 7.6% less. [ABSTRACT FROM AUTHOR]

Background: The gain or loss of large chromosomal regions or even whole chromosomes is termed as genomic scarring and can be observed as copy number variations resulting from the failure of DNA damage repair. Results: In this study, a new algorithm called genomic scar analysis (GSA) has developed and validated to calculate homologous recombination deficiency (HRD) score. The two critical submodules were tree recursion (TR) segmentation and filtering, and the estimation and correction of the tumor purity and ploidy. Then, this study evaluated the rationality of segmentation and genotype identification by the GSA algorithm and compared with other two algorithms, PureCN and ASCAT, found that the segmentation result of GSA algorithm was more logical. In addition, the results indicated that the GSA algorithm had an excellent predictive effect on tumor purity and ploidy, if the tumor purity was more than 20%. Furtherly, this study evaluated the HRD scores and BRCA1/2 deficiency status of 195 clinical samples, and the results indicated that the accuracy was 0.98 (comparing with Affymetrix OncoScan™ assay) and the sensitivity was 95.2% (comparing with BRCA1/2 deficiency status), both were well-behaved. Finally, HRD scores and 16 genes mutations (TP53 and 15 HRR pathway genes) were analyzed in 17 cell lines, the results showed that there was higher frequency in HRR pathway genes in high HRD score samples. Conclusions: This new algorithm, named as GSA, could effectively and accurately calculate the purity and ploidy of tumor samples through NGS data, and then reflect the degree of genomic instability and large-scale copy number variations of tumor samples. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) has emerged as one of the key technical aspects of the fifth-generation (5G) networks. The integration of MEC with resource-constrained unmanned aerial vehicles (UAVs) greatly enables flexible resource provisioning for supporting dynamic and computation-intensive UAV applications. Existing resource trading could facilitate this paradigm with proper incentives, which, however, may often incur unexpected negotiation latency and energy consumption, trading failures and unfair pricing, due to the unpredictable nature of the resource trading process. Motivated by these challenges, an efficient futures-enabled resource trading mechanism for edge computing-assisted UAV network is proposed, where a mutually beneficial and risk-tolerable forward contract is devised to promote resource trading between an MEC server (seller) and a UAV (buyer) with multiple tasks. Two key problems i.e. futures contract design before trading, and transmission power optimization during trading are studied. By analyzing historical statistics associated with future resource supply, demand, and air-to-ground communication quality, the contract design is formulated as a multi-objective optimization problem aiming to maximize both the seller’s and the buyer’s expected utilities, while estimating their acceptable risk tolerance. Accordingly, we propose an efficient bilateral negotiation scheme to help players reach a trading consensus on the amount of resources and the relevant price. For the power optimization problem, we develop a practical algorithm that enables the buyer to determine its optimal transmission power via convex optimization techniques. Comprehensive simulations demonstrate that the proposed mechanism offers mutually beneficial utilities to players, while achieving commendable performance on trading failures and fairness, negotiation latency and cost, comparing with baseline methods. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to accurately and efficiently identifying a large number of wireless devices blindly installed to known three-dimensional installation/fitting points, based on received signal strengths (RSSs) between the devices. The approach is non-trivial because of the factorial, mapping nature of the considered problem, the multiplicative ranging errors of the RSS measurements (with standard deviation of 5–7 dB), and the requirement of high mapping accuracy in many internet-of-things (IoT) applications, e.g., industrial IoT and aircraft. The consideration of a structured environment where the set of candidate node positions is known beforehand shifts the problem of interest from a pure position estimation problem to a position assignment problem. The key idea of the proposed approach is that we interpret the position mapping problem with a probabilistic graphical model, where the factorial nature of mapping (more specifically, the mutual exclusiveness of devices at every installation point) is fully captured. A max-product belief propagation is designed against the probabilistic graph, to estimate the max-marginal position distribution of each device. The Kuhn-Munkres algorithm is applied to preserve the mutual exclusiveness of devices throughout the belief propagation and to decide device locations based on the estimated position distributions. Large-scale simulations and field tests are carried out, showing that the new approach achieves close-to-100% accuracy in simulations with hundreds of blindfolded devices under RSS measurement errors with standard deviation of 5–7 dB. Our approach also achieves 100% accuracy in all field trials with 76 devices inside the cabin of a Fokker 100 airplane, dramatically outperforming baseline techniques. [ABSTRACT FROM AUTHOR]

Automatic modulation recognition is a critical challenge in the field of cognitive radio. In the process of communication, radio signals are modulated in various modes and are interfered by the complex electromagnetic environment. To cope with these problems and avoid manual selection of complex expert features, we propose a multi-level feature extraction algorithm based on deep learning to adequately exploit the hidden feature information of modulated signals. Our algorithm integrates the correlation between the channels of radio signals with convolutional neural networks and Bidirectional Long Short-term Memory (Bi-LSTM), and adopts the appropriate skip connection, which avoids the loss of valid information and achieves the complementarity between spatial and temporal features. In our model, the one-dimensional convolutional layer is specially utilized to enrich the feature representation of each sample point of in-phase and quadrature (I/Q) signals and emphasize the mutual influence of I channel (in-phase signal) and Q channel (quadrature signal). In addition, the label smoothing technique is used to improve the generalization ability of the model. Our proposed method is also of certain significance for other signal processing methods based on deep learning. Experiment results demonstrate that our algorithm outperforms the popular algorithms and is of higher robustness. Specifically, the proposed method improves the recognition accuracy, reaching 92.68% at high signal-to-noise ratio (SNR). In particular, it also reduces the difficulty of recognition for multiple quadrature amplitude modulation (MQAM) signals and significantly improves the recognition accuracy for 16QAM and 64QAM. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet of Things (IoT) technology, IoT devices have been widely used to collect physiological health data and provide diversified services to the terminal users. However, traditional data storage and sharing scheme cloud computing based in IoT face many challenges. For example, IoT devices are usually resource‐constrained (storage, computing power, battery capacity, etc.), data signed by IoT devices to ensure data integrity and authenticity will consume a lot of computing resources of IoT devices. At the same time, there is the challenge of high latency and unsafe data storage and sharing. To overcome these challenges, we propose a secure and efficient data storage and sharing scheme for blockchain‐based mobile‐edge computing. In our scheme, we construct the unique signature private key in a region into multiple key shares. IoT devices only need to submit the data and the random key shares allocated to the edge node. Edge node uses the recovered signature private key to realize data signature and homomorphic encryption. At the same time, the edge node will process timely data and return to the user. For data that need to be uploaded to the cloud for analysis, we use backup uploads to avoid data floods. Through experiments, it was found that our scheme can not only realize low‐latency message response for the terminal users, but also realize anonymous identity verification while ensuring data integrity and authenticity. The key shares of the signature private key are stored in different blocks of the blockchain to improve fault tolerance. The content extraction signature algorithm ensures that the key shares stored in different blocks are publicly verifiable. Safety analysis and performance analysis verify the feasibility and effectiveness of our scheme. [ABSTRACT FROM AUTHOR]

This paper presents a two-step optimization-based design method for iterative learning control and applies it onto the quadrotor unmanned aerial vehicles (UAVs) trajectory tracking problem. Iterative learning control aims to improve the tracking performance through learning from errors over iterations in repetitively operated systems. The tracking errors from previous iterations are injected into a learning filter and a robust filter to generate the learning signal. The design of the two filters usually involves nontrivial tuning work. This paper presents a new two-optimization design method for the iterative learning control, which is easy to obtain and implement. In particular, the learning filter design problem is transferred into a feedback controller design problem for a purposely constructed system, which is solved based on H-infinity optimal control theory thereafter. The robust filter is then obtained by solving an additional optimization to guarantee the learning convergence. Through the proposed design method, the learning performance is optimized and the system's stability is guaranteed. The proposed two-step optimization-based design method and the regarding iterative learning control algorithm are validated by both numerical and experimental studies. [ABSTRACT FROM AUTHOR]

Analyzing the runtime behaviors of Android apps is crucial for malware detection. In this paper, we attempt to learn the behavior level features of an app from function calls. The challenges of this task are twofold. First, the absence of function attributes hinders the understanding of app behaviors. Second, the graphical representation of function calls cannot be directly processed by classical machine learning algorithms. In this paper, we develop two methods to overcome these challenges. Based on function embedding, we first propose the concept of enhanced function call graphs (E-FCGs) to characterize app runtime behaviors. We then develop a Graph Convolutional Network (GCN) based algorithm to obtain vector representations of E-FCGs. Extensive experiments show that the features learned by our method can achieve surprisingly high detection performance on a variety of classifiers (e.g., LR, DT, SVM, KNN, RF, MLP and CNN), significantly outperforming the traditional static features. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we propose a filtering algorithm for simultaneously estimating the mode, input and state of hidden mode switched linear stochastic systems with unknown inputs. Using a multiple‐model approach with a bank of linear input and state filters for each mode, our algorithm relies on the ability to find the most probable model as a mode estimate, which we show is possible with input and state filters by identifying a key property, that a particular residual signal we call generalized innovation is a Gaussian white noise. We also provide an asymptotic analysis for the proposed algorithm and provide sufficient conditions for asymptotically achieving convergence to the true model (consistency), or to the "closest" model according to an information‐theoretic measure (convergence). A simulation example of intention‐aware vehicles at an intersection is given to demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

In recent decades, more teachers are using question generators to provide students with online homework. Learning-to-rank (LTR) methods can partially rank questions to address the needs of individual students and reduce their study burden. Unfortunately, ranking questions for students is not trivial because of three main challenges: (1) discovering students' latent knowledge and cognitive level is difficult, (2) the content of quizzes can be totally different but the knowledge points of these quizzes may be inherently related, and (3) ranking models based on supervised, semisupervised, or reinforcement learning focus on the current assignment without considering past performance. In this work, we propose KFRank, a knowledge-fusion ranking model based on reinforcement learning, which considers both a student's assignment history and the relevance of quizzes with their knowledge points. First, we load students' assignment history, reorganize it using knowledge points, and calculate the effective features for ranking in terms of the relation between a student's knowledge cognitive and the question. Then, a similarity estimator is built to choose historical questions, and an attention neural network is used to calculate the attention value and update the current study state with knowledge fusion. Finally, a rank algorithm based on a Markov decision process is used to optimize the parameters. Extensive experiments were conducted on a real-life dataset spanning a year and we compared our model with the state-of-the-art ranking models (e.g., ListNET and LambdaMART) and reinforcement-learning methods (such as MDPRank). Based on top- k nDCG values, our model outperforms other methods for groups of average and weak students, whose study abilities are relatively poor and thus their behaviors are more difficult to predict. [ABSTRACT FROM AUTHOR]

Approaching to the biological neural network, small-world neural networks have been demonstrated to improve the generalization performance of artificial neural networks. However, the architecture of small-world neural networks is typically large and predefined. This may cause the problems of overfitting and time consuming, and cannot obtain an optimal network structure automatically for a given problem. To solve the above problems, this paper proposes a pruning feedforward small-world neural network (PFSWNN), and applies it to nonlinear system modeling. Firstly, a feedforward small-world neural network (FSWNN) is constructed according to the rewiring rule of Watts–Strogatz. Secondly, the importance of each hidden neuron is evaluated based on its Katz centrality. If the Katz centrality of a hidden neuron is below the predefined threshold, this neuron is considered to be an unimportant node and then merged with its most correlated neuron in the same hidden layer. The connection weights are trained using the gradient-based algorithm, and the convergence of the proposed PFSWNN is theoretically analyzed in this paper. Finally, the PFSWNN model is tested on some problems for nonlinear system modeling, including the approximation for a rapidly changing function, CATS missing time-series prediction, four benchmark problems of UCI public datasets and a practical problem for wastewater treatment process. Experimental results demonstrate that PFSWNN exhibits superior generalization performance by small-world property as well as the pruning algorithm, and the training time of PFSWNN is shortened owning to a compact structure. • The importance of the hidden nodes is measured using Katz centrality. • The small-worldness property and pruning improve the generalization performance of PFSWNN. • PFSWNN can self-adjust to a compact structure by the pruning algorithm. • The convergence of PFSWNN can be guaranteed. [ABSTRACT FROM AUTHOR]

To accurately estimate the state of charge (SOC) of lithium-ion power batteries in the event of errors in the battery model or unknown external noise, an SOC estimation method based on the H-infinity filter (HIF) algorithm is proposed in this paper. Firstly, a fractional-order battery model based on a dual polarization equivalent circuit model is established. Then, the parameters of the fractional-order battery model are identified by the hybrid particle swarm optimization (HPSO) algorithm, based on a genetic crossover factor. Finally, the accuracy of the SOC estimation results of the lithium-ion batteries, using the HIF algorithm and extended Kalman filter (EKF) algorithm, are verified and compared under three conditions: uncertain measurement accuracy, uncertain SOC initial value, and uncertain application conditions. The simulation results show that the SOC estimation method based on HIF can ensure that the SOC estimation error value fluctuates within ±0.02 in any case, and is slightly affected by environmental and other factors. It provides a way to improve the accuracy of SOC estimation in a battery management system. [ABSTRACT FROM AUTHOR]

Protein cysteine S-sulfenylation is an essential and reversible post-translational modification that plays a crucial role in transcriptional regulation, stress response, cell signaling and protein function. Studies have shown that S-sulfenylation is involved in many human diseases such as cancer, diabetes and arteriosclerosis. However, experimental identification of protein S-sulfenylation sites is generally expensive and time-consuming. In this study, we proposed a new protein S-sulfenylation sites prediction method SulSite-GTB. First, fusion of amino acid composition, dipeptide composition, encoding based on grouped weight, K nearest neighbors, position-specific amino acid propensity, position-weighted amino acid composition and pseudo-position specific score matrix feature extraction to obtain the initial feature space. Secondly, we use the synthetic minority oversampling technique (SMOTE) algorithm to process the class imbalance data, and the least absolute shrinkage and selection operator (LASSO) are employed to remove the redundant and irrelevant features. Finally, the optimal feature subset is input into the gradient tree boosting classifier to predict the S-sulfenylation sites, and the five-fold cross-validation and independent test set method are used to evaluate the prediction performance of the model. Experimental results showed the overall prediction accuracy is 92.86% and 88.53%, respectively, and the AUC values are 0.9706 and 0.9425, respectively, on the training set and the independent test set. Compared with other prediction methods, the results show that the proposed method SulSite-GTB is significantly superior to other state-of-the-art methods and provides a new idea for the prediction of post-translational modification sites of other proteins. The source code and all datasets are available at https://github.com/QUST-AIBBDRC/SulSite-GTB/. [ABSTRACT FROM AUTHOR]

The universal blind quantum computation (UBQC) is a scheme to allow a client to delegate a computation to a remote server while concealing the input, output and algorithm. However, the qubit errors are inevitable in the practical application. In this paper, a fault-tolerant quantum error correction code preparation protocol with weak coherent pulses is proposed for fault-tolerant UBQC. Furthermore, the ϵ -correctness and ϵ -blindness of the protocol are fully proven. The simulation results show that the required number of pulses in our protocol is much less than that of the remote blind qubit state preparation protocol with two decoy states in case of the same probability of successful preparation, and is closer to asymptotic case. [ABSTRACT FROM AUTHOR]

A new proportional integral derivative (PID) control method is proposed for the 3D laser scanning system converted from 2D Lidar with a pitching motion device. It combines the advantages of a fuzzy algorithm, a radial basis function (RBF) neural network and a predictive algorithm to control the pitching motion of 2D Lidar quickly and accurately. The proposed method adopts the RBF neural network and feedback compensation to eliminate the unknown nonlinear part in the Lidar pitching motion, adaptively adjusting the PID parameter by a fuzzy algorithm. Then, the predictive control algorithm is adopted to optimize the overall controller output in real time. Finally, the simulation results show that the step response time of the Lidar pitching motion system using the control method is reduced from 15.298 s to 1.957 s with a steady-state error of 0.07°. Meanwhile, the system still has favorable response performance for the sinusoidal and step inputs under model mismatch and large disturbance. Therefore, the control method proposed above can improve the system performance and control the pitching motion of the 2D Lidar effectively. [ABSTRACT FROM AUTHOR]

The multi-angle polarimetric (MAP) satellite measurements provide abundant information concerning aerosol optical/microphysical properties. In this study, we present a robust and flexible aerosol retrieval algorithm for MAP measurements based on physics-informed deep learning (PDL) method. Different from optimized inversion that needs iterative Radiative Transfer (RT) calculations of all the unknowns, the PDL method can model the whole MAP observations with each retrieved aerosol parameter separately with the pre-training of RT simulations. Furthermore, the training of PDL can make full use of the prior information from ground-based aerosol inversions and satellite surface products, and provides an effective constraint to avoid unphysical values. To examine performance of PDL algorithm, we retrieve aerosols over eastern China from POLDER-3 measurements during 2007–2009. Comparison with AERONET products shows high correlations (R > 0.91) for both POLDER-3 PDL Aerosol Optical Depth (AOD) and fine AOD. Despite lower correlations caused by a small portion of poor retrievals, PDL coarse AOD and Single Scattering Albdeo (SSA) is very consistent with AERONET results. Also, PDL retrievals perform well as the best estimates of optimized methods such as GRASP (Generalized Retrieval of Aerosol and Surface Properties). With an outstanding performance in accuracy and efficiency, the flexible PDL algorithm exhibits great potential for operational retrieval of MAP satellite measurements. • A robust aerosol algorithm is developed for MAP satellite observation. • The training of PDL method can make full use of existing priori information. • POLDER-3 PDL retrievals over eastern China have a good performance. [ABSTRACT FROM AUTHOR]

The MODIS Multiple Angle Implication of Atmospheric Correction (MAIAC) algorithm enables simultaneous retrieval of aerosol and bidirectional surface reflectance at high resolution of 1 km. Taking advantage of multi-angle and image-based information, the MAIAC algorithm has great potential for improving retrieval of aerosols over both dark and bright surfaces. Here, by comparing MAIAC aerosol products with the ground-based observations at 9 typical sites spread out in China, we gain the insights regarding the performance of MAIAC algorithm, for the first time, over Asia that has complicated surface types, diverse aerosol sources, and heavy loading of aerosols in the atmosphere. While aerosol products from MAIAC show similar spatial distribution as that from MODIS Dark-Target (DT) and Deep-Blue (DB) algorithms, they are superior to reveal numerous hotspots of high AOD values in fine scales due to their higher resolution at 1 km. Moreover, since MAIAC algorithm for cloud screening uses time series of observations, it shows higher effectiveness to mask cloudy pixels as well as the pixels of the melting and aging ice/snow surfaces. While MAIAC and ground-observed AOD values show high correlation coefficient of ∼0.94 in two AERONET sites of Beijing and Xianghe, considerable bias is prevalent in other regions of China. Systematic underestimation is found over the deserts in western China likely due to the high bias of single scattering properties of aerosol model prescribed in MAIAC algorithm. In eastern China, the distinct positive bias is found in conditions with low-moderate AOD values and likely results from errors in regression coefficients in the surface reflectance model. Given its advantages in cloud and snow/ice screening and retrieval in fine spatial resolution, MAIAC algorithm can be improved by further refinement of regional aerosol and surface properties. • First comprehensive insight into performance of MAIAC aerosol algorithm in complicated background in China. • Accuracy of MAIAC retrievals exhibits distinct spatial variations and prevalent bias. • Aerosol and surface assumptions of MAIAC algorithm needs to be improved by considering regional variations. [ABSTRACT FROM AUTHOR]

Unless the targeted mesh is developable, metric distortion is inevitable during the process of surface mesh parameterization, thus one important objective of all involved parametric studies is to reduce the metric distortion. In order to further reduce area and angle distortion, a novel method of boundary-free mesh parameterization is presented in the paper. Firstly, the initial boundary-fixed conformal parameterization from 3D surface mesh patch to a plane is performed in the method. Then, based on the initial parameterization, the iterations of boundary-free quasi-harmonic parameterization are developed, where the tensor field is updated in each iterative step and the principal curvature direction is utilized to terminate the iteration. The solution of the novel method is convenient to calculate since it involves a series of linear systems. In our novel parameterization method, lower metric distortion and considerable efficiency have been obtained in experiments. [ABSTRACT FROM AUTHOR]

To solve the narrow bandwidth and high error rate of video transmission on the wireless channel, this paper reviews the current video coding standard and improves the original code rate control algorithm. The improvement mainly includes modifying the calculation method for the first quantization parameter QPst and enhancing the effect of the previous group of pictures (GOP) on the st of the current GOP. The improved algorithm effectively overcomes the constraint of the channel bandwidth of wireless sensor network (WSN) on the code rate outputted by the video encoder. Considering the cause of the high bit error rate of WSN channel transmission, the inaccuracy of the side matching algorithm (SMA) in processing boundary macroblocks with high difference in pixel luminance was corrected by the boundarymatching algorithm(BMA), and the secondary error concealment was introduced to better utilize the completely concealed macroblock information and improve the quality of the reconstructed image. The improved error concealment algorithm was proved more effective than the original algorithm in the quality of image reconstruction through experiments. [ABSTRACT FROM AUTHOR]

Aiming at the problem that traditional similarity calculation method is not enough to capture the similarity relationship between users, a distributed collaborative filtering recommendation algorithm based on DHT is proposed for active users in distributed recommendation system to locate nearest neighbors. The algorithm searches the user's similar neighbor information according to the "fuzzy critical value" generated by the user's extreme score, so as to improve the search efficiency of the user similar neighbors. According to the distribution of user information, the calculation method of user neighbor similarity is improved. The similarity calculation is weighted, and the setting of the weights takes into account the two factors: the similarity degree between users and the inverse user information frequency. [ABSTRACT FROM AUTHOR]

Summary: For state‐of‐charge (SOC) estimation, the resistance deterioration and continuous capacity loss can lead to erroneous estimation results. In this paper, an SOC estimator of lithium‐ion battery based on the fractional‐order model and adaptive dual Kalman filtering algorithm is proposed first. Then, to improve the accuracy of SOC estimation considering capacity loss, the particle filter algorithm is applied to update capacity online in real time. Then, an SOC estimation method is proposed considering battery capacity loss. The simulation results show that the accuracy of battery capacity prediction based on particle filter is high under the condition of capacity loss. [ABSTRACT FROM AUTHOR]

Protein-DNA interactions play important roles in regulations of many vital cellular processes, including transcription, translation, DNA replication and recombination. Sequence variants occurring in these DNA binding proteins that alter protein-DNA interactions may cause significant perturbations or complete abolishment of function, potentially leading to diseases. Developing a mechanistic understanding of impacts of variants on protein-DNA interactions becomes a persistent need. To address this need we introduce a new computational method PremPDI that predicts the effect of single missense mutation in the protein on the protein-DNA interaction and calculates the quantitative binding affinity change. The PremPDI method is based on molecular mechanics force fields and fast side-chain optimization algorithms with parameters optimized on experimental sets of 219 mutations from 49 protein-DNA complexes. PremPDI yields a very good agreement between predicted and experimental values with Pearson correlation coefficient of 0.71 and root-mean-square error of 0.86 kcal mol-1. The PremPDI server could map mutations on a structural protein-DNA complex, calculate the associated changes in binding affinity, determine the deleterious effect of a mutation, and produce a mutant structural model for download. PremPDI can be applied to many tasks, such as determination of potential damaging mutations in cancer and other diseases. PremPDI is available at . [ABSTRACT FROM AUTHOR]

In order to solve the problems of different need of service quality and rescue level in disaster point during the process of cross-regional emergency, modeling and simulation of the covering problem of emergency rescue force layout is researched in this paper. First, complex management and social problem is transferred into mathematical problems, and mathematical model is founded by graph theory algorithm and statistics, and multi-level and multi-location model is founded from the thought of multi-service and multiple coverage. Then the model is simulated based on factual examples (matlab2012b 0-1 integer programming algorithm), and the scheme of different location layouts under different deployment strategies is obtained from the simulation results. At last, the simulation results is analyzed, and the validity of model is proved by the algorithm case, thus provides reference basis for the location selection decision under multi regional collaborative linkage model of cross regional emergency. [ABSTRACT FROM AUTHOR]

The present article aims at establishing formal connections between correspondence phenomena, well known from the area of modal logic, and the theory of display calculi, originated by Belnap. These connections have been seminally observed and exploited by Marcus Kracht, in the context of his characterization of the modal axioms (which he calls primitive formulas) which can be effectively transformed into 'analytic' structural rules of display calculi. In this context, a rule is 'analytic' if adding it to a display calculus preserves Belnap's cut-elimination theorem. In recent years, the state-of-the-art in correspondence theory has been uniformly extended from classical modal logic to diverse families of non-classical logics, ranging from (bi-)intuitionistic (modal) logics, linear, relevant and other substructural logics, to hybrid logics and mu-calculi. This generalization has given rise to a theory called unified correspondence, the most important technical tools of which are the algorithm ALBA, and the syntactic characterization of Sahlqvist-type classes of formulas and inequalities which is uniform in the setting of normal DLE-logics (logics the algebraic semantics of which is based on bounded distributive lattices). We apply unified correspondence theory, with its tools and insights, to extend Kracht's results and prove his claims in the setting of DLE-logics. The results of the present article characterize the space of properly displayable DLE-logics. [ABSTRACT FROM AUTHOR]