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Internet of Things (IoT) realizes the interconnection of heterogeneous devices by the technology of wireless and mobile communication. The data of target regions are collected by widely distributed sensing devices and transmitted to the processing center for aggregation and analysis as the basis of IoT. The quality of IoT services usually depends on the accuracy and integrity of data. However, due to the adverse environment or device defects, the collected data will be anomalous. Therefore, the effective method of anomaly detection is the crucial issue for guaranteeing service quality. Deep learning is one of the most concerned technology in recent years which realizes automatic feature extraction from raw data. In this article, the integrated model of the convolutional neural network (CNN) and recurrent autoencoder is proposed for anomaly detection. Simple combination of CNN and autoencoder cannot improve classification performance, especially, for time series. Therefore, we utilize the two-stage sliding window in data preprocessing to learn better representations. Based on the characteristics of the Yahoo Webscope S5 dataset, raw time series with anomalous points are extended to fixed-length sequences with normal or anomaly label via the first-stage sliding window. Then, each sequence is transformed into continuous time-dependent subsequences by another smaller sliding window. The preprocessing of the two-stage sliding window can be considered as low-level temporal feature extraction, and we empirically prove that the preprocessing of the two-stage sliding window will be useful for high-level feature extraction in the integrated model. After data preprocessing, spatial and temporal features are extracted in CNN and recurrent autoencoder for the classification in fully connected networks. Empiric results show that the proposed model has better performances on multiple classification metrics and achieves preferable effect on anomaly detection. [ABSTRACT FROM AUTHOR]

• A novel confidence-aware recommender model (CARM) is proposed with representation learning. • The interactively of review is revealed to describe the interaction between user and item. • The robustness of the proposed model is verified by extensive experiments. The recommendation systems in the online platforms often suffer from the rating data sparseness and information overload issues. Previous studies on this topic often leverage review information to construct an accurate user/item latent factor. To address this issue, we propose a novel confidence-aware recommender model via review representation learning and historical rating behavior in this article. It is motived that ratings are consistent with reviews in terms of user preferences, and reviews often contain misleading comments (e.g., fake good reviews, fake bad reviews). To this end, the interaction latent factor of user and item in the framework is constructed by exploiting review information interactivity. Then, the confidence matrix, which measures the relationship between the rating outliers and misleading reviews, is employed to further improve the model accuracy and reduce the impact of misleading reviews on the model. Furthermore, the loss function is constructed by maximum a posteriori estimation theory. Finally, the mini-batch gradient descent algorithm is introduced to optimize the loss function. Experiments conducted on four real-world datasets empirically demonstrate that our proposed method outperforms the state-of-the-art methods. The proposed method also further promotes the application in learning resource adaptation. The source Python code will be available upon request. [ABSTRACT FROM AUTHOR]

Once defined, the access control policies and regulations would never be changed in a running and state transition process. However, it will give attackers the possibility of discovering vulnerabilities in the system, and the control systems lack the ability of dynamic perception of security state and risk, causing the systems to be exposed to risks. In this article, a dynamic Bell–LaPadula (BLP) model is proposed. The conditional random field (CRF) is introduced into the BLP model to optimize the rules. First, the model formalizes the security attributes, states of system, transition rules, and constraint models on the basis of the state transition of CRFs. After the historical system access logs are processed as the original dataset, a feature selection method is proposed to extract the requests and current states as feature vectors. Second, this article presents a rules training algorithm based on L-BFGS to implement the study and training of datasets, and then marks the logs in the test set through Viterbi algorithm automatically. On the base of these, a rule generation algorithm is proposed to dynamically adjust the access control rules based on the current security status and events of the system. Third, the security of CRFs-BLP is proved by theoretical analysis. Finally, the validity and accuracy of the model are verified by estimating the value of the precision, recall, and $F1$ -score. As the system threats are shown to be decreased obviously from these experiments, this dynamic model can decrease the vulnerabilities and risk effectively. [ABSTRACT FROM AUTHOR]

Semi-supervised clustering algorithms have several limitations: 1) the computation complexity of them is very high, because calculating the similarity distances of pairs of examples is time-consuming; 2) traditional semi-supervised clustering methods have not considered how to make full use of must-link and cannot-link constraints. In the clustering, the contribution of a few pairwise constraints to the clustering performance is very limited, and some may negatively affect the outcome; and 3) these methods are not effective to handle high dimensional data, especially for time series data. Up to now, few work touched semi-supervised clustering on time series data. To efficiently cluster large-scale time series data, we first tackle contract time series clustering to produce the most accurate clustering results under a contracted time. We propose a semi-supervised time series clustering framework (STSC), which integrates a fast similarity measure and a constraint propagation approach. Based on the proposed framework, two valid semi-supervised clustering algorithms including fssK-means and fssDBSCAN are designed. Experiments on 11 datasets show that our proposed method is efficient and effective for clustering large-scale time series data. [ABSTRACT FROM AUTHOR]

Copyright infringement adversely affects the interest of copyright holders of images and videos which are uploaded to different websites and peer-to-peer image sharing systems. This paper addresses the problem of detecting copyright infringement so that copyright holders are given due credit for their work. There are several images and videos that are shared every day by millions of users with some amount of modification in images and videos originally uploaded by the copyright holders such as photographers, graphic designers, and video providers. Copyright violators, who are not the original creators of multimedia content modify them using image processing and frame modification techniques such as grayscale conversion, cropping, rotation, frame compression, and frame speed manipulation. Then, upload the tampered images and videos. To address this problem, we propose an IPFS-based (InterPlanetary File System-based) decentralized peer-to-peer image and video sharing platform built on blockchain technology. We use a perceptual hash (pHash) technique to detect copyright violations of multimedia. When multimedia is to be uploaded to the IPFS, the pHash of the same content is determined and checked against existing pHash values in the blockchain network. Similarity with existing pHash values would result in the multimedia being detected as tampered with. Blockchain technology offers the advantage of non-involvement of a third party and consequently the avoidance of a single point of failure. • We present a platform of blockchain and IPFS to store multimedia objects as transactions. • The proposed framework ensures multimedia availability, immutability, and transparency. • The proposed approach is fully distributed where copyright information can be verified. [ABSTRACT FROM AUTHOR]

Internet of things (IoT) is one of the main application scenarios of 5th generation mobile networks (5G). Along with the rapid development of 5G, IoT terminal devices will create big data. Generally, IoT terminal devices are lightweight user equipments, for example, wearable devices. In order to take use of these lightweight terminal devices, it is a feasible way to outsource these created big data to the public cloud. When these data are out of the client's control, it is of crucial importance to ensure the remote data integrity. To solve the weaknesses of the existing remote data integrity checking schemes, we propose the concept of blockchain-based remote data integrity checking (RDIC) scheme for big data. The new concept makes use of blockchain technique which greatly improves the efficiency and security of RDIC. First, the system model and security definition are given for the proposed RDIC scheme by using blockchain. Then, by using efficient modular arithmetic, RSA digital signature, blockchain, etc , we construct a lightweight blockchain-based RDIC scheme. Finally, we analyze its security and performance. The detailed analysis shows that our scheme is provably secure and lightweight. • The concept of blockchain-based RDIC for IoT in 5G Networks. • The first concrete scheme for the model. • Detailed performance analysis and experimental results. [ABSTRACT FROM AUTHOR]

With the development of online learning and distance education, online learners' discussions in forums become increasingly effective to facilitate learning. Superposters, who play a more and more important role in forums, have attracted researchers' close attention. The key to the research is how to identify superposters among a large number of participants. Some studies focus on the network interaction of superposters and some content-related features but neglect the basic quality like language expression that a superposter should possess and the learning-related features like learning collaboration. Based on the analysis of online learning corpus, through network interaction and combination of the different features of N-gram, the paper proposed the superposter identification method based on the three primary features including language expression (L), content quality (C), and social network interaction (S) and the eight secondary features including learning collaboration. The paper applied the method in the real online learning forum corpus for identifying 28 preset superposters, achieving the results of P @ 15 = 1.0 , Avg.P @ 15 = 1.0 , P @ 28 = 0.86 , and Avg.P @ 28 = 0.95. Experiments showed that this was an effective superposter identification method in online learning forums. [ABSTRACT FROM AUTHOR]

The regulation of fiscal and tax policies is an imperative prerequisite for improving the regional innovation capability. In view of this, an attempt was made to select 31 provinces and cities in China as the research object from 2009 to 2018, to extract the fiscal and tax policy text encouraging innovation of the Chinese provinces and cities based on Python, and analyze their impact on regional innovation capability from both a text data and numerical data perspective. It is noteworthy that most of the provincial fiscal policies just follow the national fiscal policies. Each province does not formulate fiscal and tax policy according to its own unique characteristics. Fiscal policies and regional innovation capability exhibit significant spatial heterogeneity. Based on the results of the dynamic panel data model, it is seen that the R&D input and industrial structure are the main sources of improving innovation capability. The fiscal expenditure for science and technology, fiscal and tax policy text, macro tax burden, business tax (BT), and value-added tax (VAT) have a significant boosting effect on the regional innovation capability. However, the corporate income tax hinders the regional innovation capability. Finally, through the robustness test of invention patents, it is found that the fiscal and tax policy text, macro tax burden, and business tax still have a positive effect on invention patents, but the role of value-added tax has changed from promotion to obstruction, and the corporate income tax has become a significant obstacle on invention patents. This shows that China should build a tax system that promotes fair competition, reduce the tax burden of enterprises, encourage enterprises to conduct independent R&D, and guide enterprises in the evolution from the low-tech to high-tech innovation by improving the tax structure and fiscal technology expenditures. [ABSTRACT FROM AUTHOR]

Scheduling is a fundamental factor in managing the network resource of the Internet of things. For IoT systems such as production lines, to operate effectively, it is necessary to find an intelligent management scheme, i.e., schedule, for network resources. In this study, we focus on multiskill resource-constrained project scheduling problem (MS-RCPSP), a combinational optimization problem that has received extensive attention from the research community due to its advantages in network resource management. In recent years, many approaches have been utilized for solving this problem such as genetic algorithm and ant colony optimization. Although these approaches introduce various optimization techniques, premature convergence issue also occurs. Moreover, previous studies have only been verified on simulation data but not on real data of factories. This paper formulated the MS-RCPSP and then proposed a novel algorithm called DEM to solve it. The proposed algorithm was developed based on the differential evolution metaheuristic. Besides, we build the reallocate function to improve the solution quality so that the proposed algorithm converges rapidly to global extremum while also avoiding getting trapped in a local extremum. To evaluate the performance of the proposed algorithm, we conduct the experiment on iMOPSE, the simulated dataset used by previous authors in their research studies. In addition, DEM was verified on real dataset supported by a famous textile industry factory. Experimental results on both simulated data and real data show that the proposed algorithm not only finds a better schedule compared with related works but also can reduce the processing time of the production line currently used at the textile industry factory. [ABSTRACT FROM AUTHOR]

In recent years, fingerprint authentication systems have been extensively deployed in various applications, including attendance systems, authentications on smartphones, mobile payment authorizations, as well as various safety certifications. However, similar to the other biometric identification technologies, fingerprint recognition is vulnerable to artificial replicas made from cheap materials, such as silicon, gelatin, etc. Thus, it is especially necessary to distinguish whether a given fingerprint is a live or a spoof one prior to such authentication. In order to solve the problems above, a novel local descriptor named Weber local binary descriptor for fingerprint liveness detection (FLD) has been proposed in this paper. The method consists of two components: the local binary differential excitation component that extracts intensity-variance features and the local binary gradient orientation component that extracts orientation features. The co-occurrence probability of the two components is calculated to construct a discriminative feature vector, which is fed into support vector machine (SVM) classifiers. The effectiveness of the proposed method is intuitively analyzed on the image samples and numerically demonstrated by Mahalanobis distance. Experiments are performed on two public databases from FLD competitions from 2011 and 2013. The results have proved that the proposed method obtains the best detection accuracy among the existing image local descriptors in FLD. [ABSTRACT FROM AUTHOR]

By using Software Defined Network (SDN) technology, senor nodes can get updated program code which can provide new features, so it has received extensive attention. How to effectively spread code to each node fast is a challenge issue in wireless sensor networks (WSNs). In this paper, an Adding Active Slot joint Larger Broadcast Radius (AAS-LBR) scheme is proposed for fast code dissemination. The AAS-LBR scheme combines the energy of data collection and code dissemination, making full use of the remaining energy in the far-sink area to increase the active slot and the broadcast radius to speed up the code dissemination. The main contributions of the proposed AAS-LBR scheme are the following: (1) Make full use of the remaining energy of the far sink area to expand the broadcast radius, so that the node broadcasts a longer distance. The wide range of broadcasts makes the number of nodes receiving code more, which speeds up the spread of code dissemination. (2) AAS-LBR uses two improved methods to further reduce the number of broadcasts and speed up the code dissemination: (a) When constructing the broadcast backbone whose nodes dominate all nodes in network and are responsible for broadcasting code, the active slot is added to the next hop node in a pipeline style on the diffusion path, which enables the code dissemination process to continue without pause. Thus, the code can quickly spread to the entire broadcast backbone. (b) For the nodes in the non-broadcast backbone whose nodes are dominated by the broadcast backbone and only for receiving code, an active slot is added coincident with its broadcast backbone’ active slot, which can reduce the time required for code dissemination and reduce the number of broadcasts. A lot of performance analysis and simulation results show that compared to previous schemed, the AAS-LBR scheme can balance energy consumption, the transmission delay can be reduced 43.09–78.69%, the number of broadcasts can be reduced 44.51–86.18% and the energy efficiency is improved by about 24.5%. [ABSTRACT FROM AUTHOR]

In energy harvesting wireless sensor networks (EHWSNs), the energy tension of the network can be relieved by obtaining the energy from the surrounding environment, but the cost on hardware cannot be ignored. Therefore, how to minimize the cost of energy harvesting hardware to reduce the network deployment cost, and further optimize the network performance, is still a challenging issue in EHWSNs. In this paper, an energy conserving and transmission radius adaptive (ECTRA) scheme is proposed to reduce the cost and optimize the performance of solar-based EHWSNs. There are two main innovations of the ECTRA scheme. Firstly, an energy conserving approach is proposed to conserve energy and avoid outage for the nodes in hotspots, which are the bottleneck of the whole network. The novelty of this scheme is adaptively rotating the transmission radius. In this way, the nodes with maximum energy consumption are rotated, balancing energy consumption between nodes and reducing the maximum energy consumption in the network. Therefore, the battery storage capacity of nodes and the cost on hardware. Secondly, the ECTRA scheme selects a larger transmission radius for rotation when the node can absorb enough energy from the surroundings. The advantages of using this method are: (a) reducing the energy consumption of nodes in near-sink areas, thereby reducing the maximum energy consumption and allowing the node of the hotspot area to conserve energy, in order to prevent the node from outage. Hence, the network deployment costs can be further reduced; (b) reducing the network delay. When a larger transmission radius is used to transmit data in the network, fewer hops are needed by data packet to the sink. After the theoretical analyses, the results show the following advantages compared with traditional method. Firstly, the ECTRA scheme can effectively reduce deployment costs by 29.58% without effecting the network performance as shown in experiment analysis; Secondly, the ECTRA scheme can effectively reduce network data transmission delay by 44–71%; Thirdly, the ECTRA scheme shows a better balance in energy consumption and the maximum energy consumption is reduced by 27.89%; And lastly, the energy utilization rate is effectively improved by 30.09–55.48%. [ABSTRACT FROM AUTHOR]

The quality of service (QoS) regarding delay, lifetime and reliability is the key to the application of wireless sensor networks (WSNs). Data aggregation is a method to effectively reduce the data transmission volume and improve the lifetime of a network. In the previous study, a common strategy required that data wait in the queue. When the length of the queue is greater than or equal to the predetermined aggregation threshold (Nt) or the waiting time is equal to the aggregation timer (Tt), data are forwarded at the expense of an increase in the delay. The primary coNtributions of the proposed Adaptive Aggregation Routing (AAR) scheme are the following: (a) the senders select the forwarding node dynamically according to the length of the data queue, which effectively reduces the delay. In the AAR scheme, the senders send data to the nodes with a long data queue. The advaNtages are that first, the nodes with a long data queue need a small amouNt of data to perform aggregation; therefore, the transmiTted data can be fully utilized to make these nodes aggregate. Second, this scheme balances the aggregating and data sending load; thus, the lifetime increases. (b) An improved AAR scheme is proposed to improve the QoS. The aggregation deadline (Tt) and the aggregation threshold (Nt) are dynamically changed in the network. In WSNs, nodes far from the sink have residual energy because these nodes transmit less data than the other nodes. In the improved AAR scheme, the nodes far from the sink have a small value of Tt and Nt to reduce delay, and the nodes near the sink are set to a large value of Tt and Nt to reduce energy consumption. Thus, the end to end delay is reduced, a longer lifetime is achieved, and the residual energy is fully used. Simulation results demonstrate that compared with the previous scheme, the performance of the AAR scheme is improved. This scheme reduces the delay by 14.91%, improves the lifetime by 30.91%, and increases energy efficiency by 76.40%. [ABSTRACT FROM AUTHOR]

The Internet of things (IoT) is composed of billions of sensing devices that are subject to threats stemming from increasing reliance on communications technologies. A Trust-Based Secure Routing (TBSR) scheme using the traceback approach is proposed to improve the security of data routing and maximize the use of available energy in Energy-HarvestingWireless Sensor Networks (EHWSNs). The main contributions of a TBSR are (a) the source nodes send data and notification to sinks through disjoint paths, separately; in such a mechanism, the data and notification can be verified independently to ensure their security. (b) Furthermore, the data and notification adopt a dynamic probability of marking and logging approach during the routing. Therefore, when attacked, the network will adopt the traceback approach to locate and clear malicious nodes to ensure security. The probability of marking is determined based on the level of battery remaining; when nodes harvest more energy, the probability of marking is higher, which can improve network security. Because if the probability of marking is higher, the number of marked nodes on the data packet routing path will be more, and the sink will be more likely to trace back the data packet routing path and find malicious nodes according to this notification. When data packets are routed again, they tend to bypass these malicious nodes, which make the success rate of routing higher and lead to improved network security. When the battery level is low, the probability of marking will be decreased, which is able to save energy. For logging, when the battery level is high, the network adopts a larger probability of marking and smaller probability of logging to transmit notification to the sink, which can reserve enough storage space to meet the storage demand for the period of the battery on low level; when the battery level is low, increasing the probability of logging can reduce energy consumption. After the level of battery remaining is high enough, nodes then send the notification which was logged before to the sink. Compared with past solutions, our results indicate that the performance of the TBSR scheme has been improved comprehensively; it can effectively increase the quantity of notification received by the sink by 20%, increase energy efficiency by 11%, reduce the maximum storage capacity needed by nodes by 33.3% and improve the success rate of routing by approximately 16.30%. [ABSTRACT FROM AUTHOR]

With various emerging mobile devices, the visual content have be to resized into different sizes or aspect ratios for good viewing experiences. In this paper, we propose a new multioperator retargeting algorithm by using four retargeting operators of seam carving, cropping, warping, and scaling iteratively. To determine which retargeting operator should be used at each iteration, we adopt structural similarity (SSIM) to evaluate the similarity between the original and retargeted images. The retargeting operator sequence is constructed based on the four types of retargeting operators by an optimization process. Since the sizes of original and retargeted images are different, scale-invariant feature transform flow is used for dense correspondence between the original and retargeted images for similarity evaluation. Additionally, visual saliency is used to weight SSIM results based on the characteristics of the human visual system. Experimental results on a public image retargeting database have shown the promising performance of the proposed multioperator retargeting algorithm. [ABSTRACT FROM AUTHOR]

Modern cloud computing applications have stringent low-latency and high-throughput requirements to meet the increasingly diverse demands from customers. In edge datacenters, the popular load balancing scheme based on packet spraying, i.e., random packet spraying (RPS), makes good use of multiple equal-cost paths to ensure high link utilization and efficient transmission. However, RPS performs poorly under the bursty traffic scenario due to packet loss or even timeout. Therefore, to reduce the tail latency caused by retransmission, we design a coding-based random packet spraying named CRPS. Specifically, the source host transmits forward error correction (FEC) encoded packets and dynamically adjusts the data redundancy based on the packet loss rate. Then the switch randomly sprays the encoded packets across all equal-cost multiple paths to implement parallel transmission. In this way, once enough encoded packets from any parallel paths arrive at the destination host, the original packets can be decoded immediately to address the adverse impact of packet loss. The NS-2 simulation results show that CRPS effectively reduces the probability of timeout and significantly improves the tail flow completion time (FCT) by up to 72% compared with the state-of-the-art multipath transmission schemes. [ABSTRACT FROM AUTHOR]

Task offloading is one of the important research directions in Internet of Vehicles. Mobile edge computing and 5G heterogeneous network frameworks bring new opportunities for task offloading research. Most research works propose resource scheduling schemes and offloading strategies for task offloading within the same period. However, the allocation of resources for offloading tasks at different periods cannot be planned uniformly. The utilization and release of resources in previous periods affect the scheduling of resource allocations in later periods. To address the task offloading problem at different periods, this paper proposes the MPTO-MT algorithm. Specifically, the algorithm first determines whether the task can be offloaded in time at a certain period according to the number of candidate offloading nodes of the task in this period. Secondly, the algorithm selects the offloading node and the task transmission method. In order to select the appropriate offloading node and transmission method, we introduce the service utility and propose three heuristics of selecting the offloading node. Finally, the algorithm updates the resource information of nodes at the next period based on the utilization and release of node resources at the period, and the service time of the nodes. Comparative experiments with other baseline algorithms show that the proposed MPTO-MT algorithm improves the task completion rate by 4.55%, the timely rate of task completion by 3.57%, and shorter average service time. [ABSTRACT FROM AUTHOR]

QoS prediction is one of the key problems in Web service recommendation and selection. The context information is a dominant factor affecting QoS, but is ignored by most of existing works. In this paper, we employ the context information, from both the user side and service side, to achieve superior QoS prediction accuracy. We propose two novel prediction models, which are capable of using the context information of users and services respectively. In the user side, we use the geographical information as the user context, and identify similar neighbors for each user based on the similarity of their context. We study the mapping relationship between the similarity value and the geographical distance. In the service side, we use the affiliation information as the service context, including the company affiliation and country affiliation. In the two models, the prediction value is learned by the QoS records of a user (or a service) and the neighbors. Also, we propose an ensemble model to combine the results of the two models. We conduct comprehensive experiments in two real-world datasets, and the experimental results demonstrate the effectiveness of our models. [ABSTRACT FROM AUTHOR]

With the pervasiveness of smart phones and the advance of wireless body sensor network (BSN), mobile Healthcare (m-Healthcare), which extends the operation of Healthcare provider into a pervasive environment for better health monitoring, has attracted considerable interest recently. However, the flourish of m-Healthcare still faces many challenges including information security and privacy preservation. In this paper, we propose a secure and privacy-preserving framework combining with multilevel trust management. In our scheme, smart phone resources including computing power and energy can be opportunistically gathered to process the computing-intensive PHI (personal health information) during m-Healthcare emergency with minimal privacy disclosure. In specific, to leverage the PHI privacy disclosure and the high reliability of PHI process and transmission in m-Healthcare emergency, we introduce an efficient lightweight encryption for those users whose trust level is low, which is based on mix cipher algorithms and pair of plain text and cipher texts, and allow a medical user to decide who can participate in the opportunistic computing to assist in processing his overwhelming PHI data. Detailed security analysis and simulations show that the proposed framework can efficiently achieve user-centric privacy protection in m-Healthcare system. [ABSTRACT FROM AUTHOR]

The dynamical degradation of digital chaotic systems (DCSs) often has serious negative influences on some digital chaos-based systems and then becomes one of the bottleneck problems stopping chaos from some applications. In this paper, we first restrict the Devaney’s chaos definition to finite state sets to describe the chaotic motion of digital systems. Then, we propose a novel control method for DCSs based on the differential mean value theorem and state feedback technology. Simulation results show the effectiveness, robustness, and superiority of the proposed method. Finally, we construct a new pseudorandom number generator (PRNG) and evaluate its randomness via NIST SP800-22 and TestU01 test suites. Statistical test results show that the proposed PRNG has high reliability of randomness, thus it can be used for cryptography and other potential applications. [ABSTRACT FROM AUTHOR]

As an important task of Graph Neural Networks (GNN), graph classification has received increasing attention, as it can be widely used in numerous fields, such as protein prediction and community prediction. The GNN models for graph classification usually require aggregating the structure and feature information of an input graph into a hidden representation vector. Such a technique can be generally referred as graph pooling, which intends to maximize the removal of noise while minimize the loss of important features. Therefore, there exists a trade-off between the noise reduction and information maximization that needs to be balanced for better performance. However, the existing pooling-based GNNs for graph classification depends on the manual setting of the graph-pooling degree, making it difficult to balance the trade-off. To this end, we propose a Feature Pyramid-based Graph Convolutional Neural network for Graph Classification (FPGCN-GC), which constructs multi-scale hierarchical information fusion to reduce information loss, and achieves an adaptive feature fusion through a learnable weighted residual connection and self-attention mechanism. The superior performance of the proposed method is verified on multiple graph classification datasets, thus illustrating the effectiveness and superiority of FPGCN-GC. In addition, we visualize FPGCN-GC by T-SNE to further analyze the underlying reason for its effectiveness. [ABSTRACT FROM AUTHOR]

The intuitionistic fuzzy entropy has been widely used in measuring the uncertainty of intuitionistic fuzzy sets. In view of some counterintuitive phenomena of the existing intuitionistic fuzzy entropies, this article proposes an improved intuitionistic fuzzy entropy based on the cotangent function, which not only considers the deviation between membership and non-membership, but also expresses the hesitancy degree of decision makers. The analyses and comparison of the data show that the improved entropy is reasonable. Then, a new IF similarity measure whose value is an IF number is proposed. The intuitionistic fuzzy entropy and similarity measure are applied to the study of the expert weight in group decision making. Based on the research of the existing expert clustering and weighting methods, we summarize an intelligent expert combination weighting scheme. Through the new intuitionistic fuzzy similarity, the decision matrix is transformed into a similarity matrix, and through the analysis of threshold change rate and the design of risk parameters, reasonable expert clustering results are obtained. On this basis, each category is weighted; the experts in the category are weighted by entropy weight theory, and the total weight of experts is determined by synthesizing the two weights. This scheme provides a new method in determining the weight of experts objectively and reasonably. Finally, the method is applied to the evaluation of railway reconstruction scheme, and an example shows the feasibility of the method. [ABSTRACT FROM AUTHOR]

With the emergence of new smart technologies, including the Internet of Things, wireless media are playing an important role to connect numerous devices to fulfill the requirements of newly developed communication systems. The massive connectivity, therefore, made the wireless spectrum too crowded and gave several challenges to resisting against potential wireless jammers. Note that, the two main challenges that have always been a part of any communication system, especially in the case of wireless communication, are information security and information jamming. Carefully considering the given challenges, this study uses a new advanced anti-jamming approach, a modulation technique based on the frequency-hopping spread spectrum, which has notably high resistance accounted against various potential jammers. The objective of this study is two-fold. First, the physical channel properties are considered, and the random bits are transmitted, employing a cryptographic secured hoping-spread pattern, having a set of carrier frequencies, known at both sides of the transmission. Second, the hashing code is computed only for the key, and transmitted along the original hopset, but with distinct frequencies set. The deployed practical anti-jamming approach, therefore, computed a high efficiency to examine the information secrecy well and primarily the connection availability even in the presence of the jammers. Moreover, this study considered and modeled a communication system and evaluated the proposed system's performance, applying the theories of Shannon's entropy and Wyner's entropy (i.e. Wyner's wiretap channel), to anticipate the system's perfect secrecy, even in the worst case when jammer has unlimited computational capabilities. [ABSTRACT FROM AUTHOR]

Wireless sensor networks are the foundation of cyber physical systems. In loss and delay sensitive wireless sensor networks, there is much room for improving the performance of opportunistic routing scheme. In this article, an adaptive duty cycle control–based opportunistic routing scheme was put forward to improve the performance of the opportunistic routing scheme. Our main work is as follows: (a) the proposed duty cycle control–based opportunistic routing scheme implements the opportunistic routing in the duty cycle–based wireless sensor networks. Compared with the opportunistic routing scheme in the non-duty cycle mode, the duty cycle control–based opportunistic routing scheme can effectively reduce the energy consumption by 55.64%, although the performance in the delay is degraded. (b) Next, adaptive duty cycle control–based opportunistic routing scheme is proposed to reduce the delay of nodes in duty cycle control–based opportunistic routing. In adaptive duty cycle control–based opportunistic routing scheme, the node's duty cycle is increased in the area where the energy has remaining, so that the network delay can be reduced without reducing the network lifetime. Compared with duty cycle control–based opportunistic routing scheme, the maximum end-to-end delay of the node in the network can be reduced by 69.83%. [ABSTRACT FROM AUTHOR]

In virtualized sensor networks, virtual machines (VMs) share the same hardware for sensing service consolidation and saving power. For those VMs that reside in the same hardware, frequent interdomain data transfers are invoked for data analytics, and sensor collaboration and actuation. Traditional ways of interdomain communications are based on virtual network interfaces of bilateral VMs for data sending and receiving. Since these network communications use TCP/IP (Transmission Control Protocol/Internet Protocol) stacks, they result in lengthy communication paths and frequent kernel interactions, which deteriorate the I/O (Input/Output) performance of involved VMs. In this paper, we propose an optimized interdomain communication approach based on shared memory to improve the interdomain communication performance of multiple VMs residing in the same sensor hardware. In our approach, the sending data are shared in memory pages maintained by the hypervisor, and the data are not transferred through the virtual network interface via a TCP/IP stack. To avoid security trapping, the shared data are mapped in the user space of each VM involved in the communication, therefore reducing tedious system calls and frequent kernel context switches. In implementation, the shared memory is created by a customized shared-device kernel module that has bidirectional event channels between both communicating VMs. For performance optimization, we use state flags in a circular buffer to reduce wait-and-notify operations and system calls during communications. Experimental results show that our proposed approach can provide five times higher throughput and 2.5 times less latency than traditional TCP/IP communication via a virtual network interface. [ABSTRACT FROM AUTHOR]

RGB-D cameras offer both color and depth images of the surrounding environment, making them an attractive option for robotic and vision applications. This work introduces the BRISK_D algorithm, which efficiently combines Features from Accelerated Segment Test (FAST) and Binary Robust Invariant Scalable Keypoints (BRISK) methods. In the BRISK_D algorithm, the keypoints are detected by the FAST algorithm and the location of the keypoint is refined in the scale and the space. The scale factor of the keypoint is directly computed with the depth information of the image. In the experiment, we have made a detailed comparative analysis of the three algorithms SURF, BRISK and BRISK_D from the aspects of scaling, rotation, perspective and blur. The BRISK_D algorithm combines depth information and has good algorithm performance. [ABSTRACT FROM AUTHOR]

Software defined networks brings greater flexibility to networks and therefore generates new vitality. Thanks to the ability to update soft code to sensor nodes, wireless sensor networks (WSNs) brings profound changes to Internet of Things. However, it is a challenging issue to minimize delay and transmission times and maintain long lifetime when broadcasting data packets in high loss ratio and low duty cycle WSNs. Although there have been some research concerning code dissemination, those schemes can only achieve a tradeoff between different performances, instead of optimizing all these important performances at the same time. Therefore, in this paper we propose a new strategy that can reduce delay and transmission times simultaneously. In traditional method, the broadcasting nature of wireless communication is not sufficiently utilized. By allowing sons of the same parent node to share awake slots, the broadcasting nature is well exploited and delay is thus reduced as well as transmission times with lifetime not affected. And, as we discover there is energy surplus when collecting data in area away from sink, we further improve this strategy so that all the performances can be further bettered. Compared with traditional method, the methods we design (IFAS, BTAS and AAPS) can respectively reduce delay by 20.56%, 31.59%, 55.16% and reduce transmission times by 29.53%, 43.93%, 42.04%, while not reducing lifetime. [ABSTRACT FROM AUTHOR]

A 3D city model is critical for the construction of a digital city. One of the methods of building a 3D city model is tilt photogrammetry. In this method, oblique photography is crucial for generating the model because the visual quality of photography directly impacts the model’s visual effect. Yet, sometimes, oblique photography does not have good visual quality due to a bad season or defective photographic equipment. For example, for oblique photography taken in winter, vegetation is brown. If this photography is employed to generate the 3D model, the result would be bad visually. Yet, common methods for vegetation greening in oblique photography rely on the assistance of the infrared band, which is not available sometimes. Thus, a method for vegetation greening in winter oblique photography without the infrared band is required, which is proposed in this paper. The method was inspired by the work on CycleGAN (Cycle-consistence Adversarial Networks). In brief, the problem of turning vegetation green in winter oblique photography is considered as a style transfer problem. Summer oblique photography generally has green vegetation. By applying CycleGAN, winter oblique photography can be transferred to summer oblique photography, and the vegetation can turn green. Yet, due to the existence of “checkerboard artifacts”, the original result cannot be applied for real production. To reduce artifacts, the generator of CycleGAN is modified. As the final results suggest, the proposed method unlocks the bottleneck of vegetation greening when the infrared band is not available and artifacts are reduced. [ABSTRACT FROM AUTHOR]

Data aggregation is a widely adopted method to effectively reduce the data transmission volume and improve the lifetime of wireless sensor networks (WSNs). In the data aggregation networks, some parameters directly determine the delay of aggregation. In industrial applications, the data generated by different sensors have different requirements for delay or other QoS performance. In the previous study, a common strategy is that all kinds of data is aggregated into one frame when the condition is satisfied with a QoS requirement, which causes excessive energy consumption and severely impairs the lifetime of network. A Differentiated Data Aggregation Routing (DDAR) scheme is proposed to reduce energy consumption and guarantee that the delay could be controlled within the corresponding QoS requirement constraint. The primary contributions of the DDAR scheme are the following: (a) The DDAR scheme makes data with different QoS requirement route to the sink along the different paths. The parameters of the aggregators in each path, such as aggregation deadline ( T t ) and the aggregation threshold ( N t ), are configured according to the QoS requirements. Accordingly, energy consumption can be reduced without degrading the performance of data transmission. (b) Based on DDAR scheme, an improved DDAR scheme is proposed to further improve performance through fully utilize the residual energy in the nodes which are far from the sink. The frequency of aggregation of these nodes increases by reducing the value of T t and N t so as to further improve the energy efficiency and reduce delay. Simulation results demonstrate that compared with the previous scheme, this scheme reduces the delay by 25.01%, improves the lifetime by 55.45%, and increases energy efficiency by 83.99%. The improved DDAR scheme improves the energy efficiency by 33.97% and service guarantee rate by 10.11%. [ABSTRACT FROM AUTHOR]

For Industrial Wireless Sensor Networks (IWSNs), sending data with timely style to the stink (or control center, CC) that is monitored by sensor nodes is a challenging issue. However, in order to save energy, wireless sensor networks based on a duty cycle are widely used in the industrial field, which can bring great delay to data transmission. We observe that if the duty cycle of a small number of nodes in the network is set to 1, the sleep delay caused by the duty cycle can be effectively reduced. Thus, in this paper, a novel Portion of Nodes with Larger Duty Cycle (PNLDC) scheme is proposed to reduce delay and optimize energy efficiency for IWSNs. In the PNLDC scheme, a portion of nodes are selected to set their duty cycle to 1, and the proportion of nodes with the duty cycle of 1 is determined according to the energy abundance of the area in which the node is located. The more the residual energy in the region, the greater the proportion of the selected nodes. Because there are a certain proportion of nodes with the duty cycle of 1 in the network, the PNLDC scheme can effectively reduce delay in IWSNs. The performance analysis and experimental results show that the proposed scheme significantly reduces the delay for forwarding data by 8.9~26.4% and delay for detection by 2.1~24.6% without reducing the network lifetime when compared with the fixed duty cycle method. Meanwhile, compared with the dynamic duty cycle strategy, the proposed scheme has certain advantages in terms of energy utilization and delay reduction. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), communication links are typically error-prone and unreliable, so providing reliable and timely data routing for loss- and delay-sensitive applications in WSNs it is a challenge issue. Additionally, with specific thresholds in practical applications, the loss and delay sensitivity implies requirements for high reliability and low delay. Opportunistic Routing (OR) has been well studied in WSNs to improve reliability for error-prone and unreliable wireless communication links where the transmission power is assumed to be identical in the whole network. In this paper, a Cross-layer Optimized Opportunistic Routing (COOR) scheme is proposed to improve the communication link reliability and reduce delay for loss-and-delay sensitive WSNs. The main contribution of the COOR scheme is making full use of the remaining energy in networks to increase the transmission power of most nodes, which will provide a higher communication reliability or further transmission distance. Two optimization strategies referred to as COOR(R) and COOR(P) of the COOR scheme are proposed to improve network performance. In the case of increasing the transmission power, the COOR(R) strategy chooses a node that has a higher communication reliability with same distance in comparison to the traditional opportunistic routing when selecting the next hop candidate node. Since the reliability of data transmission is improved, the delay of the data reaching the sink is reduced by shortening the time of communication between candidate nodes. On the other hand, the COOR(P) strategy prefers a node that has the same communication reliability with longer distance. As a result, network performance can be improved for the following reasons: (a) the delay is reduced as fewer hops are needed while the packet reaches the sink in longer transmission distance circumstances; (b) the reliability can be improved since it is the product of the reliability of every hop of the routing path, and the count is reduced while the reliability of each hop is the same as the traditional method. After analyzing the energy consumption of the network in detail, the value of optimized transmission power in different areas is given. On the basis of a large number of experimental and theoretical analyses, the results show that the COOR scheme will increase communication reliability by 36.62–87.77%, decrease delay by 21.09–52.48%, and balance the energy consumption of 86.97% of the nodes in the WSNs. [ABSTRACT FROM AUTHOR]