- Jiachen Yang;Chaofan Ma;Jiabao Man;Huifang Xu;Gan Zheng;Houbing Song;
The proliferation of mobile devices that support the acceleration of data services(especially smartphones)has resulted in a dramatic increase in mobile traffic. Mobile data also increased exponentially, already exceeding the throughput of the backhaul. To improve spectrum utilization and increase mobile network traffic, in combination with content caching, we study the cooperation between primary and secondary networks via content caching. We consider that the secondary base station assists the primary user by pre-caching some popular primary contents.Thus, the secondary base station can obtain more licensed bandwidth to serve its own user. We mainly focus on the time delay from the backhaul link to the secondary base station. First, in terms of the content caching and the transmission strategies, we provide a cooperation scheme to maximize the secondary user's effective data transmission rates under the constraint of the primary users target rate. Then, we investigate the impact of the caching allocation and prove that the formulated problem is a concave problem with regard to the caching capacity allocation for any given power allocation. Furthermore, we obtain the joint caching and power allocation by an effective bisection search algorithm. Finally, our results show that the content caching cooperation scheme can achieve significant performance gain for the primary and secondary systems over the traditional two-hop relay cooperation without caching.
2020年01期 v.25 1-11页 [查看摘要][在线阅读][下载 511K] [下载次数：45 ] |[网刊下载次数：0 ] |[引用频次：2 ] |[阅读次数：0 ] - Guangzhe Zhao;Aiguo Chen;Guangxi Lu;Wei Liu;
In cyber-physical systems, multidimensional data fusion is an important method to achieve comprehensive evaluation decisions and reduce data redundancy. In this paper, a data fusion algorithm based on fuzzy set theory and Dempster-Shafer(D-S) evidence theory is proposed to overcome the shortcomings of the existing decision-layer multidimensional data fusion algorithms. The basic probability distribution of evidence is determined based on fuzzy set theory and attribute weights, and the data fusion of attribute evidence is combined with the credibility of sensor nodes in a cyber-physical systems network. Experimental analysis shows that the proposed method has obvious advantages in the degree of the differentiation of the results.
2020年01期 v.25 12-19页 [查看摘要][在线阅读][下载 237K] [下载次数：147 ] |[网刊下载次数：0 ] |[引用频次：41 ] |[阅读次数：0 ] - Kuai Xu;Feng Wang;Haiyan Wang;Bo Yang;
Fake news has recently leveraged the power and scale of online social media to effectively spread misinformation which not only erodes the trust of people on traditional presses and journalisms, but also manipulates the opinions and sentiments of the public. Detecting fake news is a daunting challenge due to subtle difference between real and fake news. As a first step of fighting with fake news, this paper characterizes hundreds of popular fake and real news measured by shares, reactions, and comments on Facebook from two perspectives:domain reputations and content understanding. Our domain reputation analysis reveals that the Web sites of the fake and real news publishers exhibit diverse registration behaviors, registration timing, domain rankings, and domain popularity. In addition, fake news tends to disappear from the Web after a certain amount of time. The content characterizations on the fake and real news corpus suggest that simply applying term frequency-inverse document frequency(tf-idf) and Latent Dirichlet Allocation(LDA) topic modeling is inefficient in detecting fake news,while exploring document similarity with the term and word vectors is a very promising direction for predicting fake and real news. To the best of our knowledge, this is the first effort to systematically study domain reputations and content characteristics of fake and real news, which will provide key insights for effectively detecting fake news on social media.
2020年01期 v.25 20-27页 [查看摘要][在线阅读][下载 438K] [下载次数：136 ] |[网刊下载次数：0 ] |[引用频次：8 ] |[阅读次数：0 ] - Donghyun Kim;Junggab Son;Daehee Seo;Yeojin Kim;Hyobin Kim;Jung Taek Seo;
This paper introduces a new fog-assisted cloud storage which can achieve much higher throughput compared to the traditional cloud-only storage architecture by reducing the traffics toward the cloud storage. The fog-storage service providers are transparency to end-users and therefore, no modification on the end-user devices is necessary. This new system is featured with(1) a stronger audit scheme which is naturally coupled with the proposed architecture and does not suffer from the replay attack and(2) a transparent and efficient compensation mechanism for the fog-storage service providers. We provide rigorous theoretical analysis on the correctness and soundness of the proposed system. To the best of our knowledge, this is the first paper to discuss about a storage data audit scheme for fog-assisted cloud storage as well as the compensation mechanism for the service providers of the fog-storage service providers.
2020年01期 v.25 28-43页 [查看摘要][在线阅读][下载 477K] [下载次数：34 ] |[网刊下载次数：0 ] |[引用频次：3 ] |[阅读次数：0 ] - Ji Li;Madhuri Siddula;Xiuzhen Cheng;Wei Cheng;Zhi Tian;Yingshu Li;
As Internet-of-Things(IoT) networks provide efficient ways to transfer data, they are used widely in data sensing applications. These applications can further include wireless sensor networks. One of the critical problems in sensor-equipped IoT networks is to design energy efficient data aggregation algorithms that address the issues of maximum value and distinct set query. In this paper, we propose an algorithm based on uniform sampling and Bernoulli sampling to address these issues. We have provided logical proofs to show that the proposed algorithms return accurate results with a given probability. Simulation results show that these algorithms have high performance compared with a simple distributed algorithm in terms of energy consumption.
2020年01期 v.25 44-55页 [查看摘要][在线阅读][下载 407K] [下载次数：63 ] |[网刊下载次数：0 ] |[引用频次：10 ] |[阅读次数：0 ]
- Jianqiang Huang;Wentao Han;Xiaoying Wang;Wenguang Chen;
A Weighted Essentially Non-Oscillatory scheme(WENO) is a solution to hyperbolic conservation laws,suitable for solving high-density fluid interface instability with strong intermittency. These problems have a large and complex flow structure. To fully utilize the computing power of High Performance Computing(HPC) systems, it is necessary to develop specific methodologies to optimize the performance of applications based on the particular system's architecture. The Sunway TaihuLight supercomputer is currently ranked as the fastest supercomputer in the world. This article presents a heterogeneous parallel algorithm design and performance optimization of a high-order WENO on Sunway TaihuLight. We analyzed characteristics of kernel functions, and proposed an appropriate heterogeneous parallel model. We also figured out the best division strategy for computing tasks,and implemented the parallel algorithm on Sunway TaihuLight. By using access optimization, data dependency elimination, and vectorization optimization, our parallel algorithm can achieve up to 172× speedup on one single node, and additional 58× speedup on 64 nodes, with nearly linear scalability.
2020年01期 v.25 56-67页 [查看摘要][在线阅读][下载 759K] [下载次数：66 ] |[网刊下载次数：0 ] |[引用频次：10 ] |[阅读次数：0 ] - Jiaxi Gu;Jiliang Wang;Lan Zhang;Zhiwen Yu;Xiaozhe Xin;Yunhao Liu;
Camera-equipped mobile devices are encouraging people to take more photos and the development and growth of social networks is making it increasingly popular to share photos online. When objects appear in overlapping Fields Of View(FOV), this means that they are drawing much attention and thus indicates their popularity. Successfully discovering and locating these objects can be very useful for many applications, such as criminal investigations, event summaries, and crowdsourcing-based Geographical Information Systems(GIS).Existing methods require either prior knowledge of the environment or intentional photographing. In this paper, we propose a seamless approach called "Spotlight", which performs passive localization using crowdsourced photos.Using a graph-based model, we combine object images across multiple camera views. Within each set of combined object images, a photographing map is built on which object localization is performed using plane geometry. We evaluate the system's localization accuracy using photos taken in various scenarios, with the results showing our approach to be effective for passive object localization and to achieve a high level of accuracy.
2020年01期 v.25 68-80页 [查看摘要][在线阅读][下载 1175K] [下载次数：24 ] |[网刊下载次数：0 ] |[引用频次：2 ] |[阅读次数：0 ] - Yudong Qin;Deke Guo;Xu Lin;Geyao Cheng;
Visible-Light Communication(VLC) has the potential to provide dense and fast connectivity at low cost. In this paper we propose SFNet, a novel VLC-enabled hybrid data center network that extends the design of wireless Data Center Networks(DCNs) into three further dimensions:(1) fully wireless at the inter-rack level;(2) no need for a centralized control mechanism on wireless links; and(3) no need for any infrastructure-level alterations to data centers. Previous proposals typically cannot realize these three rationales simultaneously. To achieve this vision,the proposed SFNet augments fat-tree by organizing all racks into a wireless small-world network via VLC links. The use of VLC links eliminates hierarchical switches and cables in the wireless network, and thus reduces hardware investment and maintenance costs. To fully exploit the benefits of the topology of SFNet, we further propose its topology design and optimization method, routing scheme, and online flow scheduling algorithm. Comprehensive experiments indicate that SFNet exhibits good topological properties and network performance.
2020年01期 v.25 81-92页 [查看摘要][在线阅读][下载 1079K] [下载次数：39 ] |[网刊下载次数：0 ] |[引用频次：3 ] |[阅读次数：0 ] - Wen Zhou;Jinyuan Jia;Chengxi Huang;Yongqing Cheng;
With the rapid development of Web3 D technologies, sketch-based model retrieval has become an increasingly important challenge, while the application of Virtual Reality and 3 D technologies has made shape retrieval of furniture over a web browser feasible. In this paper, we propose a learning framework for shape retrieval based on two Siamese VGG-16 Convolutional Neural Networks(CNNs), and a CNN-based hybrid learning algorithm to select the best view for a shape. In this algorithm, the AlexNet and VGG-16 CNN architectures are used to perform classification tasks and to extract features, respectively. In addition, a feature fusion method is used to measure the similarity relation of the output features from the two Siamese networks. The proposed framework can provide new alternatives for furniture retrieval in the Web3 D environment. The primary innovation is in the employment of deep learning methods to solve the challenge of obtaining the best view of 3 D furniture,and to address cross-domain feature learning problems. We conduct an experiment to verify the feasibility of the framework and the results show our approach to be superior in comparison to many mainstream state-of-the-art approaches.
2020年01期 v.25 93-102页 [查看摘要][在线阅读][下载 551K] [下载次数：74 ] |[网刊下载次数：0 ] |[引用频次：5 ] |[阅读次数：0 ] - Tun Li;Qinhan Yu;Hai Wan;Sikun Li;
Soft errors have become a critical challenge as a result of technology scaling. Existing circuit-hardening techniques are commonly associated with prohibitive overhead of performance, area, and power. However,evaluating the influence of soft errors in Flip-Flops(FFs) on the failure of circuit is a difficult verification problem.Here, we proposed a novel flip-flop soft-error failure rate analysis methodology using a formal method with respect to application behaviors. Approach and optimization techniques to implement the proposed methodology based on the given formula using Sequential Equivalence Checking(SEC) are introduced. The proposed method combines the advantage of formal technique-based approaches in completeness and the advantage of application behaviors in accuracy to differentiate vulnerability of components. As a result, the FFs in a circuit are sorted by their failure rates, and designers can use this information to perform optimal hardening of selected sequential components against soft errors. Experimental results of an implementation of a SpaceWire end node and the largest ISCAS'89 benchmark sequential circuits indicate the feasibility and potential scalability of our approach. A case study on an instruction decoder of a practical 32-bit microprocessor demonstrates the applicability of our method.
2020年01期 v.25 103-116页 [查看摘要][在线阅读][下载 738K] [下载次数：29 ] |[网刊下载次数：0 ] |[引用频次：0 ] |[阅读次数：0 ] - Xu Han;Binyang Li;Zhuoran Wang;
Uncertainty identification is an important semantic processing task. It is crucial to the quality of information in terms of factuality in many applications, such as topic detection and question answering. Factuality has become a premier concern especially in social media, in which texts are written informally. However, existing approaches that rely on lexical cues suffer greatly from the casual or word-of-mouth peculiarity of social media, in which the cue phrases are often expressed in substandard form or even omitted from sentences. To tackle these problems, this paper proposes an Attention-based Neural Framework for Uncertainty identification on social media texts, named ANFU. ANFU incorporates attention-based Long Short-Term Memory(LSTM) networks to represent the semantics of words and Convolutional Neural Networks(CNNs) to capture the most important semantics. Experiments were conducted on four datasets, including 2 English benchmark datasets used in the CoNLL-2010 task of uncertainty identification and 2 Chinese datasets of Weibo and Chinese news texts. Experimental results showed that our proposed ANFU approach outperformed the-state-of-the-art on all the datasets in terms of F1 measure. More importantly, 41.37% and 13.10% improvements were achieved over the baselines on English and Chinese social media datasets, respectively, showing the particular effectiveness of ANFU on social media texts.
2020年01期 v.25 117-126页 [查看摘要][在线阅读][下载 285K] [下载次数：59 ] |[网刊下载次数：0 ] |[引用频次：10 ] |[阅读次数：0 ] - Jiayang Li;Yi Zhang;Mengkai Shi;Qi Liu;Yi Chen;
We analyzed and improved a collision avoidance strategy, which was supported by Long Term EvolutionVehicle(LTE-V)-based Vehicle-to-Vehicle(V2 V) communication, for automated vehicles. This work was completed in two steps. In the first step, we analyzed the probability distribution of message transmission time, which was conditional on transmission distance and vehicle density. Our analysis revealed that transmission time exhibited a near-linear increase with distance and density. We also quantified the trade-off between high/low resource reselection probabilities to improve the setting of media access parameters. In the second step, we studied the required safety distance in accordance with the response time, i.e., the transmission time, derived on the basis of a novel concept of Responsibility-Sensitive Safety(RSS). We improved the strategy by considering the uncertainty of response time and its dependence on vehicle distance and density. We performed theoretical analysis and numerical testing to illustrate the effectiveness of the improved robust RSS strategy. Our results enhance the practicability of building driverless highways with special lanes reserved for the exclusive use of LTE-V vehicles.
2020年01期 v.25 127-139页 [查看摘要][在线阅读][下载 803K] [下载次数：127 ] |[网刊下载次数：0 ] |[引用频次：15 ] |[阅读次数：0 ] - Pei Zhang;Changqing An;Zhanfeng Wang;Fengyuan Ma;
The performance of Internet applications is heavily affected by the end-to-end available bandwidth. Thus,it is very important to examine how to accurately predict the available Internet bandwidth. A number of available bandwidth prediction algorithms have been proposed to date, but none of the existing solutions are able to achieve a high level of accuracy. In this paper, a Multi-manifold based Available Bandwidth prediction algorithm(MD-AVB)is proposed, based on the observation that the available bandwidth space on the Internet is multi-manifold and asymmetrical. In the proposed algorithm, the available bandwidth space is divided into multiple lower-dimensional domains iteratively, and each domain is embedded separately to predict the available bandwidth. Experiments on HP S~3 datasets demonstrate that the proposed algorithm is more accurate than existing approaches.
2020年01期 v.25 140-148页 [查看摘要][在线阅读][下载 940K] [下载次数：25 ] |[网刊下载次数：0 ] |[引用频次：3 ] |[阅读次数：0 ] - Gang Li;Yi Zhao;Ling Zhang;Xingwei Wang;Yueqin Zhang;Fayun Guo;
This paper proposes a parameter adaptive hybrid model for image segmentation. The hybrid model combines the global and local information in an image, and provides an automated solution for adjusting the selection of the two weight parameters. Firstly, it combines an improved local model with the global Chan-Vese(CV) model, while the image's local entropy is used to establish the index for measuring the image's gray-level information. Parameter adjustment is then performed by the real-time acquisition of the ratio of the different functional energy in a self-adapting model responsive to gray-scale distribution in the image segmentation process.Compared with the traditional linear adjustment model, which is based on trial-and-error, this paper presents a more quantitative and intelligent method for achieving the dynamic nonlinear adjustment of global and local terms.Experiments show that the proposed model achieves fast and accurate segmentation for different types of noisy and non-uniform grayscale images and noise images. Moreover, the method demonstrates high stability and is insensitive to the position of the initial contour.
2020年01期 v.25 149-160页 [查看摘要][在线阅读][下载 855K] [下载次数：95 ] |[网刊下载次数：0 ] |[引用频次：6 ] |[阅读次数：0 ]