Xi’an University of Posts and Telecommunications

Mobile users typically have high demand on localized and location-based information services. To always retrieve the localized data from the remote cloud, however, tends to be inefficient, which motivates fog computing. The fog computing, also known as edge computing, extends cloud computing by deploying localized computing facilities at the premise of users, which prestores cloud data and distributes to mobile users with fast-rate local connections. As such, fog computing introduces an intermediate fog layer between mobile users and cloud, and complements cloud computing toward low-latency high-rate services to mobile users. In this fundamental framework, it is important to study the interplay and cooperation between the edge (fog) and the core (cloud). In this paper, the tradeoff between power consumption and transmission delay in the fog-cloud computing system is investigated. We formulate a workload allocation problem which suggests the optimal workload allocations between fog and cloud toward the minimal power consumption with the constrained service delay. The problem is then tackled using an approximate approach by decomposing the primal problem into three subproblems of corresponding subsystems, which can be, respectively, solved. Finally, based on simulations and numerical results, we show that by sacrificing modest computation resources to save communication bandwidth and reduce transmission latency, fog computing can significantly improve the performance of cloud computing.

Electronic Health Records (EHRs) are entirely controlled by hospitals instead of patients, which complicates seeking medical advices from different hospitals. Patients face a critical need to focus on the details of their own healthcare and restore management of their own medical data. The rapid development of blockchain technology promotes population healthcare, including medical records as well as patient-related data. This technology provides patients with comprehensive, immutable records, and access to EHRs free from service providers and treatment websites. In this paper, to guarantee the validity of EHRs encapsulated in blockchain, we present an attribute-based signature scheme with multiple authorities, in which a patient endorses a message according to the attribute while disclosing no information other than the evidence that he has attested to it. Furthermore, there are multiple authorities without a trusted single or central one to generate and distribute public/private keys of the patient, which avoids the escrow problem and conforms to the mode of distributed data storage in the blockchain. By sharing the secret pseudorandom function seeds among authorities, this protocol resists collusion attack out of N from N -1 corrupted authorities. Under the assumption of the computational bilinear Diffie-Hellman, we also formally demonstrate that, in terms of the unforgeability and perfect privacy of the attribute-signer, this attribute-based signature scheme is secure in the random oracle model. The comparison shows the efficiency and properties between the proposed method and methods proposed in other studies.

With the rapid development of the Internet of Things and cloud computing technologies, smart health (s-health) is expected to significantly improve the quality of health care. However, data security and user privacy concerns in s-health have not been adequately addressed. As a well-received solution to realize fine-grained access control, ciphertext-policy attribute-based encryption (CP-ABE) has the potential to ensure data security in s-health. Nevertheless, direct adoption of the traditional CP-ABE in s-health suffers two flaws. For one thing, access policies are in cleartext form and reveal sensitive health-related information in the encrypted s-health records (SHRs). For another, it usually supports small attribute universe, which places an undesirable limitation on practical deployments of CP-ABE because the size of its public parameters grows linearly with the size of the universe. To address these problems, we introduce PASH, a privacy-aware s-health access control system, in which the key ingredient is a large universe CP-ABE with access policies partially hidden. In PASH, attribute values of access policies are hidden in encrypted SHRs and only attribute names are revealed. In fact, attribute values carry much more sensitive information than generic attribute names. Particularly, PASH realizes an efficient SHR decryption test which needs a small number of bilinear pairings. The attribute universe can be exponentially large and the size of public parameters is small and constant. Our security analysis indicates that PASH is fully secure in the standard model. Performance comparisons and experimental results show that PASH is more efficient and expressive than previous schemes.

Abstract The increasing demand for a whiter smile has resulted in an increased popularity for tooth whitening procedures. The most classic hydrogen peroxide-based whitening agents are effective, but can lead to enamel demineralization, gingival irritation, or cytotoxicity. Furthermore, these techniques are excessively time-consuming. Here, we report a nondestructive, harmless and convenient tooth whitening strategy based on a piezo-catalysis effect realized by replacement of abrasives traditionally used in toothpaste with piezoelectric particles. Degradation of organic dyes via piezo-catalysis of BaTiO 3 (BTO) nanoparticles was performed under ultrasonic vibration to simulate daily tooth brushing. Teeth stained with black tea, blueberry juice, wine or a combination thereof can be notably whitened by the poled BTO turbid liquid after vibration for 3 h. A similar treatment using unpoled or cubic BTO show negligible tooth whitening effect. Furthermore, the BTO nanoparticle-based piezo-catalysis tooth whitening procedure exhibits remarkably less damage to both enamel and biological cells.

Extreme learning machine proposed by Huang G-B has attracted many attentions for its extremely fast training speed and good generalization performance. But it still can be considered as empirical risk minimization theme and tends to generate over-fitting model. Additionally, since ELM doesn't considering heteroskedasticity in real applications, its performance will be affected seriously when outliers exist in the dataset. In order to address these drawbacks, we propose a novel algorithm called regularized extreme learning machine based on structural risk minimization principle and weighted least square. The generalization performance of the proposed algorithm was improved significantly in most cases without increasing training time.

Abstract In this study, mechanical vibration is used for hydrogen generation and decomposition of dye molecules, with the help of BiFeO 3 (BFO) square nanosheets. A high hydrogen production rate of ≈124.1 μmol g −1 is achieved under mechanical vibration (100 W) for 1 h at the resonant frequency of the BFO nanosheets. The decomposition ratio of Rhodamine B dye reaches up to ≈94.1 % after mechanical vibration of the BFO catalyst for 50 min. The vibration‐induced catalysis of the BFO square nanosheets may be attributed to the piezocatalytic properties of BFO and the high specific surface area of the nanosheets. The uncompensated piezoelectric charges on the surfaces of BFO nanosheets induced by mechanical vibration result in a built‐in electric field across the nanosheets. Unlike a photocatalyst for water splitting, which requires a proper band edge position for hydrogen evolution, such a requirement is not needed in piezocatalytic water splitting, where the band tilting under the induced piezoelectric field will make the conduction band of BFO more negative than the H 2 /H 2 O redox potential (0 V) for hydrogen generation.

The Internet of Vehicles (IoV) is a promising branch of the Internet of Things. IoV simulates a large variety of crowdsourcing applications, such as Waze, Uber, and Amazon Mechanical Turk, etc. Users of these applications report the real-time traffic information to the cloud server which trains a machine learning model based on traffic information reported by users for intelligent traffic management. However, crowdsourcing application owners can easily infer users' location information, traffic information, motor vehicle information, environmental information, etc., which raises severe sensitive personal information privacy concerns of the users. In addition, as the number of vehicles increases, the frequent communication between vehicles and the cloud server incurs unexpected amount of communication cost. To avoid the privacy threat and reduce the communication cost, in this article, we propose to integrate federated learning and local differential privacy (LDP) to facilitate the crowdsourcing applications to achieve the machine learning model. Specifically, we propose four LDP mechanisms to perturb gradients generated by vehicles. The proposed Three-Outputs mechanism introduces three different output possibilities to deliver a high accuracy when the privacy budget is small. The output possibilities of Three-Outputs can be encoded with two bits to reduce the communication cost. Besides, to maximize the performance when the privacy budget is large, an optimal piecewise mechanism (PM-OPT) is proposed. We further propose a suboptimal mechanism (PM-SUB) with a simple formula and comparable utility to PM-OPT. Then, we build a novel hybrid mechanism by combining Three-Outputs and PM-SUB. Finally, an LDP-FedSGD algorithm is proposed to coordinate the cloud server and vehicles to train the model collaboratively. Extensive experimental results on real-world data sets validate that our proposed algorithms are capable of protecting privacy while guaranteeing utility.

Abstract All‐inorganic double‐metal perovskite materials have recently gained much attention due to their three dimensionality (3D) and non‐toxic nature to replace lead‐based perovskite materials. Among all those double perovskite materials, theoretical works have demonstrated that Cs 2 AgBiBr 6 shows high stability and possesses a suitable band gap for solar‐cell applications. However, the film‐forming ability of Cs 2 AgBiBr 6 is found to be the utmost challenge hindering its development in thin‐film solar‐cell devices. In this work, a high‐quality Cs 2 AgBiBr 6 film with ultra‐smooth morphology, micro‐sized grains, and high crystallinity is realized via anti‐solvent dropping technology and post‐annealing at high temperature. After optimization, the first example of an inverted planar heterojunction solar‐cell device based on Cs 2 AgBiBr 6 exhibits a power conversion efficiency of 2.23 % with V OC =1.01 V, J SC =3.19 mA/cm 2 , and FF=69.2 %. Besides, the device shows no hysteresis and a high stability.

Rapid advances in new generation information technologies, such as big data analytics, internet of things (IoT), edge computing and artificial intelligence, have nowadays driven traditional manufacturing all the way to intelligent manufacturing. Intelligent manufacturing is characterised by autonomy and self-optimisation, which proposes new demands such as learning and cognitive capacities for manufacturing cell, known as the minimum implementation unit for intelligent manufacturing. Consequently, this paper proposes a general framework for knowledge-driven digital twin manufacturing cell (KDTMC) towards intelligent manufacturing, which could support autonomous manufacturing by an intelligent perceiving, simulating, understanding, predicting, optimising and controlling strategy. Three key enabling technologies including digital twin model, dynamic knowledge bases and knowledge-based intelligent skills for supporting the above strategy are analysed, which equip KDTMC with the capacities of self-thinking, self-decision-making, self-execution and self-improving. The implementing methods of KDTMC are also introduced by a thus constructed test bed. Three application examples about intelligent process planning, intelligent production scheduling and production process analysis and dynamic regulation demonstrate the feasibility of KDTMC, which provides a practical insight into the intelligent manufacturing paradigm.

Mobile-edge computing (MEC) is a promising paradigm to improve the quality of computation experience of mobile devices because it allows mobile devices to offload computing tasks to MEC servers, benefiting from the powerful computing resources of MEC servers. However, the existing computation-offloading works have also some open issues: 1) security and privacy issues; 2) cooperative computation offloading; and 3) dynamic optimization. To address the security and privacy issues, we employ the blockchain technology that ensures the reliability and irreversibility of data in MEC systems. Meanwhile, we jointly design and optimize the performance of blockchain and MEC. In this article, we develop a cooperative computation offloading and resource allocation framework for blockchain-enabled MEC systems. In the framework, we design a multiobjective function to maximize the computation rate of MEC systems and the transaction throughput of blockchain systems by jointly optimizing offloading decision, power allocation, block size, and block interval. Due to the dynamic characteristics of the wireless fading channel and the processing queues at MEC servers, the joint optimization is formulated as a Markov decision process (MDP). To tackle the dynamics and complexity of the blockchain-enabled MEC system, we develop an asynchronous advantage actor–critic-based cooperation computation offloading and resource allocation algorithm to solve the MDP problem. In the algorithm, deep neural networks are optimized by utilizing asynchronous gradient descent and eliminating the correlation of data. The simulation results show that the proposed algorithm converges fast and achieves significant performance improvements over existing schemes in terms of total reward.

Driven by the ever-increasing requirements of autonomous vehicles, such as traffic monitoring and driving assistant, deep learning-based object detection (DL-OD) has been increasingly attractive in intelligent transportation systems. However, it is difficult for the existing DL-OD schemes to realize the responsible, cost-saving, and energy-efficient autonomous vehicle systems due to low their inherent defects of low timeliness and high energy consumption. In this paper, we propose an object detection (OD) system based on edge-cloud cooperation and reconstructive convolutional neural networks, which is called Edge YOLO. This system can effectively avoid the excessive dependence on computing power and uneven distribution of cloud computing resources. Specifically, it is a lightweight OD framework realized by combining pruning feature extraction network and compression feature fusion network to enhance the efficiency of multi-scale prediction to the largest extent. In addition, we developed an autonomous driving platform equipped with NVIDIA Jetson for system-level verification. We experimentally demonstrate the reliability and efficiency of Edge YOLO on COCO2017 and KITTI data sets, respectively. According to COCO2017 standard datasets with a speed of 26.6 frames per second (FPS), the results show that the number of parameters in the entire network is only 25.67 MB, while the accuracy (mAP) is up to 47.3%.

In the era of big data, recommender system (RS) has become an effective information filtering tool that alleviates information overload for Web users. Collaborative filtering (CF), as one of the most successful recommendation techniques, has been widely studied by various research institutions and industries and has been applied in practice. CF makes recommendations for the current active user using lots of users’ historical rating information without analyzing the content of the information resource. However, in recent years, data sparsity and high dimensionality brought by big data have negatively affected the efficiency of the traditional CF-based recommendation approaches. In CF, the context information, such as time information and trust relationships among the friends, is introduced into RS to construct a training model to further improve the recommendation accuracy and user’s satisfaction, and therefore, a variety of hybrid CF-based recommendation algorithms have emerged. In this paper, we mainly review and summarize the traditional CF-based approaches and techniques used in RS and study some recent hybrid CF-based recommendation approaches and techniques, including the latest hybrid memory-based and model-based CF recommendation algorithms. Finally, we discuss the potential impact that may improve the RS and future direction. In this paper, we aim at introducing the recent hybrid CF-based recommendation techniques fusing social networks to solve data sparsity and high dimensionality and provide a novel point of view to improve the performance of RS, thereby presenting a useful resource in the state-of-the-art research result for future researchers.

The proliferation of smart vehicular terminals (VTs) and their resource hungry applications impose serious challenges to the processing capabilities of VTs and the delivery of vehicular services. Mobile Edge Computing (MEC) offers a promising paradigm to solve this problem by offloading VT applications to proximal MEC servers, while TV white space (TVWS) bands can be used to supplement the bandwidth for computation offloading. In this paper, we consider a cognitive vehicular network that uses the TVWS band, and formulate a dual-side optimization problem, to minimize the cost of VTs and that of the MEC server at the same time. Specifically, the dual-side cost minimization is achieved by jointly optimizing the offloading decision and local CPU frequency on the VT side, and the radio resource allocation and server provisioning on the server side, while guaranteeing network stability. Based on Lyapunov optimization, we design an algorithm called DDORV to tackle the joint optimization problem, where only current system states, such as channel states and traffic arrivals, are needed. The closed-form solution to the VT-side problem is obtained easily by derivation and comparing two values. For MEC server side optimization, we first obtain server provisioning independently, and then devise a continuous relaxation and Lagrangian dual decomposition based iterative algorithm for joint radio resource and power allocation. Simulation results demonstrate that DDORV converges fast, can balance the cost-delay tradeoff flexibly, and can obtain more performance gains in cost reduction as compared with existing schemes.

In view of the lack of feature complementarity between the feature layers of Single Shot MultiBox Detector (SSD) and the weak detection ability of SSD for small objects, we propose an improved SSD object detection algorithm based on Dense Convolutional Network (DenseNet) and feature fusion, which is called DF-SSD. On the basis of SSD, we design the feature extraction network DenseNet-S-32-1 with reference to the dense connection of DenseNet, and replace the original backbone network VGG-16 of SSD with DenseNet-S-32-1 to enhance the feature extraction ability of the model. In the part of multi-scale detection, a fusion mechanism of multi-scale feature layers is introduced to organically combine low-level visual features and high-level semantic features in the network structure. Finally, a residual block is established before the object prediction to further improve the model performance. We train the DF-SSD model from scratch. The experimental results show that our model DF-SSD with 300 × 300 input achieves 81.4% mAP, 79.0% mAP, and 29.5% mAP on PASCAL VOC 2007, VOC 2012, and MS COCO datasets, respectively. Compared with SSD, the detection accuracy of DF-SSD on VOC 2007 is improved by 3.1% mAP. DF-SSD requires only 1/2 parameters to SSD and 1/9 parameters to Faster RCNN. We inject more semantic information into DF-SSD, which makes it have advanced detection effect on small objects and objects with specific relationships.

Immersive virtual reality (VR) video is becoming increasingly popular owing to its enhanced immersive experience. To enjoy ultrahigh resolution immersive VR video with wireless user equipments, such as head-mounted displays (HMDs), ultralow-latency viewport rendering, and data transmission are the core prerequisites, which could not be achieved without a huge bandwidth and superior processing capabilities. Besides, potentially very high energy consumption at the HMD may impede the rapid development of wireless panoramic VR video. Multiaccess edge computing (MEC) has emerged as a promising technology to reduce both the task processing latency and the energy consumption for HMD, while bandwidth-rich terahertz (THz) communication is expected to enable ultrahigh-speed wireless data transmission. In this article, we propose to minimize the long-term energy consumption of a THz wireless access-based MEC system for high quality immersive VR video services support by jointly optimizing the viewport rendering offloading and downlink transmit power control. Considering the time-varying nature of wireless channel conditions, we propose a deep reinforcement learning-based approach to learn the optimal viewport rendering offloading and transmit power control policies and an asynchronous advantage actor-critic (A3C)-based joint optimization algorithm is proposed. The simulation results demonstrate that the proposed algorithm converges fast under different learning rates, and outperforms existing algorithms in terms of minimized energy consumption and maximized reward.

Most of the approaches used for Landslide inventory mapping (LIM) rely on traditional feature extraction and unsupervised classification algorithms. However, it is difficult to use these approaches to detect landslide areas because of the complexity and spatial uncertainty of landslides. In this letter, we propose a novel approach based on a fully convolutional network within pyramid pooling (FCN-PP) for LIM. The proposed approach has three advantages. First, this approach is automatic and insensitive to noise because multivariate morphological reconstruction is used for image preprocessing. Second, it is able to take into account features from multiple convolutional layers and explore efficiently the context of images, which leads to a good tradeoff between wider receptive field and the use of context. Finally, the selected PP module addresses the drawback of global pooling employed by convolutional neural network, FCN, and U-Net, and, thus, provides better feature maps for landslide areas. Experimental results show that the proposed FCN-PP is effective for LIM, and it outperforms the state-of-the-art approaches in terms of five metrics, $Precision$ , $Recall$ , $Overall~Error$ , $F$ -$score$ , and $Accuracy$ .

Unmanned aerial vehicles (UAVs) can be employed as aerial base stations to support communication for the ground users (GUs). However, the aerial-to-ground (A2G) channel link is dominated by line-of-sight (LoS) due to the high flying altitude, which is easily wiretapped by the ground eavesdroppers (GEs). In this case, a single UAV has limited maneuvering capacity to obtain the desired secure rate in the presence of multiple eavesdroppers. In this paper, we propose a cooperative jamming approach by letting UAV jammers help the UAV transmitter defend against GEs. To be specific, the UAV transmitter sends the confidential information to GUs, and the UAV jammers send the artificial noise signals to the GEs by 3D beamforming. We propose a multi-agent deep reinforcement learning (MADRL) approach, i.e., multi-agent deep deterministic policy gradient (MADDPG) to maximize the secure capacity by jointly optimizing the trajectory of UAVs, the transmit power from UAV transmitter and the jamming power from the UAV jammers. The MADDPG algorithm adopts centralized training and distributed execution. The simulation results show the MADRL method can realize the joint trajectory design of UAVs and achieve good performance. To improve the learning efficiency and convergence, we further propose a continuous action attention MADDPG (CAA-MADDPG) method, where the agent learns to pay attention to the actions and observations of other agents that are more relevant with it. From the simulation results, the rewards performance of CAA-MADDPG is better than the MADDPG without attention.

The existing definition of generalized intuitionistic fuzzy soft sets (GIFSSs) is clarified and reformulated as a combination of an IFSS over the universe of discourse and an intuitionistic fuzzy set in the parameter set. With this new perspective, two different types of generalized intuitionistic fuzzy soft subsets and various new operations are developed for GIFSSs. The upper and lower substitutions for both intuitionistic fuzzy sets and GIFSSs are defined as well. Some existing notions and results are improved by virtue of these new concepts. Using the expectation score function, two binary relations are proposed for comparing intuitionistic fuzzy values. An algorithm is designed for coping with multiattribute decision making (MADM) problems with a combined use of the GIFSS, the extended intersection operation, the intuitionistic fuzzy weighted averaging operator, and other related notions. A case study concerning a faculty appointment problem is conducted to illustrate the proposed algorithm. Moreover, a comparative analysis between our approach and other related works is given to demonstrate the effectiveness and advantages of the newly proposed method.

This paper proves that the development of the digital economy has become a new vector to promote the upgrading of China’s industrial structure. In addition, heterogeneous technological innovation plays an intermediary role in the promotion of the industrial structure by the digital economy. This study aims to solve the following: whether the development level of the digital economy is positively promoting the upgrading of the industrial structure; whether technological innovation can promote the upgrading of the industrial structure; the path of the digital economy through which to promote the upgrading of the industrial structure and the heterogeneity of this path. The purpose of this study was to verify the digital economy and scientific and technological innovation to promote the upgrading of the industrial structure and the reality of the realization path; and to solve the problem of insufficient power for upgrading China’s regional industrial structure against the background of the impact of the new generation of information technology. This study mainly adopted comprehensive evaluation and multivariate statistical analysis methods. The statistical basis for the study was data from 30 Chinese provinces from 2013 to 2018. The results confirm the hypothesis that the development of the digital economy and scientific and technological innovation have a positive role in promoting the upgrading of the industrial structure, and also prove that the intermediary role of heterogeneous technological innovation is crucial in the process of the digital economy promoting industrial upgrading. This conclusion can further give play to the role of the digital economy in promoting industrial structure upgrading, build a clean and intelligent industrial chain, solve the root cause of the lack of new drivers for China’s industrial upgrading, and help to form a new development pattern of domestic and international double circulation, so as to achieve the high-quality and sustainable development of China’s economy.

Some evidence suggests that bone health can be regulated by gut microbiota. To better understand this, we performed 16S ribosomal RNA sequencing to analyze the intestinal microbial diversity in primary osteoporosis (OP) patients, osteopenia (ON) patients and normal controls (NC). We observed an inverse correlation between the number of bacterial taxa and the value of bone mineral density. The diversity estimators in the OP and ON groups were increased compared with those in the NC group. Beta diversity analyses based on hierarchical clustering and principal coordinate analysis (PCoA) could discriminate the NC samples from OP and ON samples. Firmicutes, Bacteroidetes, Proteobacteria and Actinobacteria constituted the four dominant phyla in all samples. Proportion of Firmicutes was significantly higher and Bacteroidetes was significantly lower in OP samples than that in NC samples ( p < 0.05), Gemmatimonadetes and Chloroflexi were significantly different between OP and NC group as well as between ON and NC group ( p < 0.01). A total of 21 genera with proportions above 1% were detected and Bacteroides accounted for the largest proportion in all samples. The Blautia, Parabacteroides and Ruminococcaceae genera differed significantly between the OP and NC group ( p < 0.05). Linear discriminant analysis (LDA) results showed one phylum community and seven phylum communities were enriched in ON and OP, respectively. Thirty-five genus communities, five genus communities and two genus communities were enriched in OP, ON and NC, respectively. The results of this study indicate that gut microbiota may be a critical factor in osteoporosis development, which can further help us search for novel biomarkers of gut microbiota in OP and understand the interaction between gut microbiota and bone health.