Intelligent and secure fog-aided internet of drones

Internet of drones (IoD), which utilize drones as Internet of Things (IoT) devices, deploys several drones in the air to collect ground information and send them to the IoD gateway for further processing. Computing tasks are usually offloaded to the cloud data center for intensive processing. However, many IoD applications require real-time processing and event response (e.g., disaster response and virtual reality applications). Hence, data processing by the remote cloud may not satisfy the strict latency requirement. Fog computing attaches fog nodes, which are equipped with computing, storage and networking resources, to IoD gateways to assume a substantial amount of computing tasks instead of performing all tasks in the remote cloud, thus enabling immediate service response. Fog-aided IoD provisions future events prediction and image classification by machine learning technologies, where massive training data are collected by drones and analyzed in the fog node. However, the performance of IoD is greatly affected by drones\u27 battery capacities. Also, aggregating all data in the fog node may incur huge network traffic and drone data privacy leakage. To address the challenge of limited drone battery, the power control problem is first investigated in IoD for the data collection service to minimize the energy consumption of a drone while meeting the quality of service (QoS) requirements. A PowEr conTROL (PETROL) algorithm is then proposed to solve this problem and its convergence rate is derived. The task allocation (which distributes tasks to different fog nodes) and the flying control (which adjusts the drone\u27s flying speed) are then jointly optimized to minimize the drone\u27s journey completion time constrained by the drone\u27s battery capacity and task completion deadlines. In consideration of the practical scenario that the future task information is difficult to obtain, an online algorithm is designed to provide strategies for task allocation and flying control when the drone visits each location without knowing the future. The joint optimization of power control and energy harvesting control is also studied to determine each drone\u27s transmission power and the transmitted energy from the charging station in the time-varying IoD network. The objective is to minimize the long-term average system energy cost constrained by the drones\u27 battery capacities and QoS requirements. A Markov Decision Process (MDP) is formulated to characterize the power and energy harvesting control process in time-varying IoD networks. A modified actor-critic reinforcement learning algorithm is then proposed to tackle the problem. To address the challenge of drone data privacy leakage, federated learning (FL) is proposed to preserve drone data privacy by performing local training in drones and sharing training model parameters with a fog node without uploading drone raw data. However, drone privacy can still be divulged to ground eavesdroppers by wiretapping and analyzing uploaded parameters during the FL training process. The power control problem of all drones is hence investigated to maximize the FL system security rate constrained by drone battery capacities and the QoS requirements (e.g., FL training time). This problem is formulated as a non-linear programming problem and an algorithm is designed to obtain the optimum solutions with low computational complexity. All proposed algorithms are demonstrated to perform better than existing algorithms by extensive simulations and can be implemented in the intelligent and secure fog-aided IoD network to improve system performances on energy efficiency, QoS, and security

Feature Selective Networks for Object Detection

Objects for detection usually have distinct characteristics in different sub-regions and different aspect ratios. However, in prevalent two-stage object detection methods, Region-of-Interest (RoI) features are extracted by RoI pooling with little emphasis on these translation-variant feature components. We present feature selective networks to reform the feature representations of RoIs by exploiting their disparities among sub-regions and aspect ratios. Our network produces the sub-region attention bank and aspect ratio attention bank for the whole image. The RoI-based sub-region attention map and aspect ratio attention map are selectively pooled from the banks, and then used to refine the original RoI features for RoI classification. Equipped with a light-weight detection subnetwork, our network gets a consistent boost in detection performance based on general ConvNet backbones (ResNet-101, GoogLeNet and VGG-16). Without bells and whistles, our detectors equipped with ResNet-101 achieve more than 3% mAP improvement compared to counterparts on PASCAL VOC 2007, PASCAL VOC 2012 and MS COCO datasets

SMEs’ Entrepreneurship from the Perspective of Social Networks

Companies utilize social networks which don\u27t entail any additional resources to promote their products, services as well as brands, build a brand image and handle customer relationships. Therefore, numerous SMEs are more likely to turn to social media when they launch a business. The current research mainly uses questionnaires or case studies to illustrate the benefits resulted from using social media by SMEs to start up a business. A large amount of information flow in social media has brought a lot of opportunities to SMEs. Still, meantime it also puts more pressure on SMEs that lack funds and technology to use such information. In the end, whether social media brings benefits or disadvantages to entrepreneurship still needs empirical data to confirm. From this perspective, this article looks for empirical data to demonstrate the role of social media in entrepreneurship for SMEs. This study obtains relevant data of sample companies from e-commerce and social media websites and applies the data envelopment model to measure the efficiency of these enterprises using social media entrepreneurship

Introduction and Consideration of the Lost Document Tang Li in Shodō Kanmon Shinkyō : Focusing on Tong Yufu of the Tang Dynasty

The collection of documents on the divine mirror (one of the three Japanese imperial regalia) entitled Shodō Kanmon Shinkyō, is thought to have been written by the hakase shodō scholars of the imperial academies. Many excerpts and references to literary Japanese works can be seen in these documents. Because there are references to fish-shaped Tong Yu authorization tallies in the lost history documents, known as Tang Li - which are quoted in the Shodō Kanmon Shinkyō - they became a focus of attention and appeared often in other works as well. The Tong Yu tallies are related to fish-shaped Yu Fu and Yu Dai authorization tallies, which were prevalent in the Tang period. In this paper, after providing an overview of the divine mirror and permission to recast the divine mirror, which was burnt in a fire in 1040, I　will introduce fragments from the lost Tang Li. By doing so, my goals is to delineate the changes in the Tong Yufu tallies in the Sui and Tang periods and examine their relationship with the recasting of the divine mirror of the Japanese imperial regalia.研究ノートStudy Note

柳芳撰『唐暦』の成立・展開と日本における伝来の研究 [全文の要約]

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Minimum profile Hellinger distance estimation for a semiparametric mixture model

In this paper, we propose a new effective estimator for a class of semiparametric mixture models where one component has known distribution with possibly unknown parameters while the other component density and the mixing proportion are unknown. Such semiparametric mixture models have been often used in multiple hypothesis testing and the sequential clustering algorithm. The proposed estimator is based on the minimum profile Hellinger distance (MPHD), and its theoretical properties are investigated. In addition, we use simulation studies to illustrate the finite sample performance of the MPHD estimator and compare it with some other existing approaches. The empirical studies demonstrate that the new method outperforms existing estimators when data are generated under contamination and works comparably to existing estimators when data are not contaminated. Applications to two real data sets are also provided to illustrate the effectiveness of the new methodology

Avoidance Behavior toward Social Network Advertising: Dimensions and Measurement

While social network advertising is pervasive, research focused on avoidance behavior toward it is relatively rare. This study provides the development of a three-dimension scale to measure avoidance behavior toward social network advertising. Based on the survey of 195 social network users, evidence is provided for the reliability, factor structure and validity. Meanwhile, T-tests are used to examine the effects of gender, sample source and purchasing experience on the three-dimension avoidance behavior (i.e., skimming, ignoring and blocking). The results show males on social network are more likely to block social network advertising than females while users without purchasing experience on social network are more likely to skimming through advertisements on social network

A New Load Transfer Model of Skin Friction for Super-long Pile Under Axially Load

In present investigation, a new load transfer model was proposed, in which the softening and strengthening of super-long pile skin friction was considered. The influence of parameters variation on the softening load transfer model was discussed in detail. The load transfer model proposed was verified by engineering results. The nonlinear iterative calculation method of the super-long piles was improved with considering the nonlinear compression model of concrete and the weight of the pile body. Comparing calculation results and the engineering measured data, it demonstrates that the relationship between pile skin friction and the relative displacement and the settlement results are generally in good agreement with the practical engineering results. The skin friction of super-long piles increases with the increase of the depth, while decreases with the increase of the depth near the end of the pile and it has an obvious downward trend. As the load continues to increase, the skin friction near the pile end increases significantly and shows a gradual expansion of the pattern. It completely reflects the softening and strengthening properties of pile skin friction