Efficient Multi-Authority Attribute-Based Searchable Encryption Scheme with Blockchain Assistance for Cloud-Edge Coordination

Cloud storage and edge computing are utilized to address the storage and computational challenges arising from the exponential data growth in IoT.However,data privacy is potentially risky when data is outsourced to cloud servers or edge services.While data encryption ensures data confidentiality,it can impede data sharing and retrieval.Attribute-based searchable encryption(ABSE)is proposed as an effective technique for enhancing data security and privacy.Nevertheless,ABSE has its limitations,such as single attribute authorization failure,privacy leakage during the search process,and high decryption overhead.This paper presents a novel approach called the blockchain-assisted efficientmulti-authority attribute-based searchable encryption scheme(BEM-ABSE)for cloudedge collaboration scenarios to address these issues.BEM-ABSE leverages a consortium blockchain to replace the central authentication center for global public parameter management.It incorporates smart contracts to facilitate reliable and fair ciphertext keyword search and decryption result verification.To minimize the computing burden on resource-constrained devices,BEM-ABSE adopts an online/offline hybrid mechanism during the encryption process and a verifiable edge-assisted decryption mechanism.This ensures both low computation cost and reliable ciphertext.Security analysis conducted under the random oracle model demonstrates that BEM-ABSE is resistant to indistinguishable chosen keyword attacks(IND-CKA)and indistinguishable chosen plaintext attacks(INDCPA).Theoretical analysis and simulation results confirm that BEM-ABSE significantly improves computational efficiency compared to existing solutions.

Anomaly Detection and Access Control for Cloud-Edge Collaboration Networks

Software-defined networking(SDN)enables the separation of control and data planes,allowing for centralized control and management of the network.Without adequate access control methods,the risk of unau-thorized access to the network and its resources increases significantly.This can result in various security breaches.In addition,if authorized devices are attacked or controlled by hackers,they may turn into malicious devices,which can cause severe damage to the network if their abnormal behaviour goes undetected and their access privileges are not promptly restricted.To solve those problems,an anomaly detection and access control mechanism based on SDN and neural networks is proposed for cloud-edge collaboration networks.The system employs the Attribute Based Access Control(ABAC)model and smart contract for fine-grained control of device access to the network.Furthermore,a cloud-edge collaborative Key Performance Indicator(KPI)anomaly detection method based on the Gated Recurrent Unit and Generative Adversarial Nets(GRU-GAN)is designed to discover the anomaly devices.An access restriction mechanism based on reputation value and anomaly detection is given to prevent anomalous devices.Experiments show that the proposed mechanism performs better anomaly detection on several datasets.The reputation-based access restriction effectively reduces the number of malicious device attacks.

Pedestrian and Vehicle Detection Based on Pruning YOLOv4 with Cloud-Edge Collaboration

Nowadays,the rapid development of edge computing has driven an increasing number of deep learning applications deployed at the edge of the network,such as pedestrian and vehicle detection,to provide efficient intelligent services to mobile users.However,as the accuracy requirements continue to increase,the components of deep learning models for pedestrian and vehicle detection,such as YOLOv4,become more sophisticated and the computing resources required for model training are increasing dramatically,which in turn leads to significant challenges in achieving effective deployment on resource-constrained edge devices while ensuring the high accuracy performance.For addressing this challenge,a cloud-edge collaboration-based pedestrian and vehicle detection framework is proposed in this paper,which enables sufficient training of models by utilizing the abundant computing resources in the cloud,and then deploying the well-trained models on edge devices,thus reducing the computing resource requirements for model training on edge devices.Furthermore,to reduce the size of the model deployed on edge devices,an automatic pruning method combines the convolution layer and BN layer is proposed to compress the pedestrian and vehicle detection model size.Experimental results show that the framework proposed in this paper is able to deploy the pruned model on a real edge device,Jetson TX2,with 6.72 times higher FPS.Meanwhile,the channel pruning reduces the volume and the number of parameters to 96.77%for the model,and the computing amount is reduced to 81.37%.

Deep reinforcement learning based multi-level dynamic reconfiguration for urban distribution network:a cloud-edge collaboration architecture

With the construction of the power Internet of Things(IoT),communication between smart devices in urban distribution networks has been gradually moving towards high speed,high compatibility,and low latency,which provides reliable support for reconfiguration optimization in urban distribution networks.Thus,this study proposed a deep reinforcement learning based multi-level dynamic reconfiguration method for urban distribution networks in a cloud-edge collaboration architecture to obtain a real-time optimal multi-level dynamic reconfiguration solution.First,the multi-level dynamic reconfiguration method was discussed,which included feeder-,transformer-,and substation-levels.Subsequently,the multi-agent system was combined with the cloud-edge collaboration architecture to build a deep reinforcement learning model for multi-level dynamic reconfiguration in an urban distribution network.The cloud-edge collaboration architecture can effectively support the multi-agent system to conduct“centralized training and decentralized execution”operation modes and improve the learning efficiency of the model.Thereafter,for a multi-agent system,this study adopted a combination of offline and online learning to endow the model with the ability to realize automatic optimization and updation of the strategy.In the offline learning phase,a Q-learning-based multi-agent conservative Q-learning(MACQL)algorithm was proposed to stabilize the learning results and reduce the risk of the next online learning phase.In the online learning phase,a multi-agent deep deterministic policy gradient(MADDPG)algorithm based on policy gradients was proposed to explore the action space and update the experience pool.Finally,the effectiveness of the proposed method was verified through a simulation analysis of a real-world 445-node system.

Achieving Fuzzy Matching Data Sharing for Secure Cloud-Edge Communication

In this paper,we propose a novel fuzzy matching data sharing scheme named FADS for cloudedge communications.FADS allows users to specify their access policies,and enables receivers to obtain the data transmitted by the senders if and only if the two sides meet their defined certain policies simultaneously.Specifically,we first formalize the definition and security models of fuzzy matching data sharing in cloud-edge environments.Then,we construct a concrete instantiation by pairing-based cryptosystem and the privacy-preserving set intersection on attribute sets from both sides to construct a concurrent matching over the policies.If the matching succeeds,the data can be decrypted.Otherwise,nothing will be revealed.In addition,FADS allows users to dynamically specify the policy for each time,which is an urgent demand in practice.A thorough security analysis demonstrates that FADS is of provable security under indistinguishable chosen ciphertext attack(IND-CCA)in random oracle model against probabilistic polynomial-time(PPT)adversary,and the desirable security properties of privacy and authenticity are achieved.Extensive experiments provide evidence that FADS is with acceptable efficiency.

异国书写中的“他者”:《虚拟偶像爱朵露》中的“城寨形象”

比较文学形象学中的“他者”是注视者笔下的异国形象。赛博朋克文学的经典之作,吉布森《虚拟偶像爱朵露》中作为“他者”的城寨形象即源于作者所处的社会与时代对中国的“集体想象”,也源于他对中国香港九龙城寨直接与间接接受中的想象与书写。而他想象和书写中的城寨形象集乐园般的乌托邦和受制于各种规则的无自由之地等特点于一身,承载着中西方文化的融合与博弈中对西方社会的反思与批判。

Market Equilibrium Based on Cloud-edge Collaboration

Market participants can only bid with lagged information disclosure under the existing market mechanism,which can lead to information asymmetry and irrational market behavior,thus influencing market efficiency.To promote rational bidding behavior of market participants and improve market efficiency,a novel electricity market mechanism based on cloudedge collaboration is proposed in this paper.Critical market information,called residual demand curve,is published to market participants in real-time on the cloud side,while participants on the edge side are allowed to adjust their bids according to the information disclosure prior to closure gate.The proposed mechanism can encourage rational bids in an incentive-compatible way through the process of dynamic equilibrium while protecting participants’privacy.This paper further formulates the mathematical model of market equilibrium to simulate the process of each market participant’s strategic bidding behavior towards equilibrium.A case study based on the IEEE 30-bus system shows the proposed market mechanism can effectively guide bidding behavior of market participants,while condensing exchanged information and protecting privacy of participants.

A cloud-edge-device collaborative offloading scheme with heterogeneous tasks and its performance evaluation

How to collaboratively offload tasks between user devices,edge networks(ENs),and cloud data centers is an interesting and challenging research topic.In this paper,we investigate the offoading decision,analytical modeling,and system parameter optimization problem in a collaborative cloud-edge device environment,aiming to trade off different performance measures.According to the differentiated delay requirements of tasks,we classify the tasks into delay-sensitive and delay-tolerant tasks.To meet the delay requirements of delay-sensitive tasks and process as many delay-tolerant tasks as possible,we propose a cloud-edge device collaborative task offoading scheme,in which delay-sensitive and delay-tolerant tasks follow the access threshold policy and the loss policy,respectively.We establish a four-dimensional continuous-time Markov chain as the system model.By using the Gauss-Seidel method,we derive the stationary probability distribution of the system model.Accordingly,we present the blocking rate of delay-sensitive tasks and the average delay of these two types of tasks.Numerical experiments are conducted and analyzed to evaluate the system performance,and numerical simulations are presented to evaluate and validate the effectiveness of the proposed task offloading scheme.Finally,we optimize the access threshold in the EN buffer to obtain the minimum system cost with different proportions of delay-sensitive tasks.

User Churn Prediction Hierarchical Model Based on Graph Attention Convolutional Neural Networks

The telecommunications industry is becoming increasingly aware of potential subscriber churn as a result of the growing popularity of smartphones in the mobile Internet era,the quick development of telecommunications services,the implementation of the number portability policy,and the intensifying competition among operators.At the same time,users'consumption preferences and choices are evolving.Excellent churn prediction models must be created in order to accurately predict the churn tendency,since keeping existing customers is far less expensive than acquiring new ones.But conventional or learning-based algorithms can only go so far into a single subscriber's data;they cannot take into consideration changes in a subscriber's subscription and ignore the coupling and correlation between various features.Additionally,the current churn prediction models have a high computational burden,a fuzzy weight distribution,and significant resource economic costs.The prediction algorithms involving network models currently in use primarily take into account the private information shared between users with text and pictures,ignoring the reference value supplied by other users with the same package.This work suggests a user churn prediction model based on Graph Attention Convolutional Neural Network(GAT-CNN)to address the aforementioned issues.The main contributions of this paper are as follows:Firstly,we present a three-tiered hierarchical cloud-edge cooperative framework that increases the volume of user feature input by means of two aggregations at the device,edge,and cloud layers.Second,we extend the use of users'own data by introducing self-attention and graph convolution models to track the relative changes of both users and packages simultaneously.Lastly,we build an integrated offline-online system for churn prediction based on the strengths of the two models,and we experimentally validate the efficacy of cloudside collaborative training and inference.In summary,the churn prediction model based on Graph Attention Convolutional Neural Network presented in this paper can effectively address the drawbacks of conventional algorithms and offer telecom operators crucial decision support in developing subscriber retention strategies and cutting operational expenses.

底流与重力流交互作用下的朵体沉积特征:以东非鲁伍马盆地中新统为例

底流与重力流的交互作用是重要的深水沉积作用类型,对海底沉积物再分配及海底地貌改造具有重要控制作用。目前对底流与水道中重力流的交互作用研究较成熟,但对底流影响下朵体的沉积特征研究较少。本研究以东非鲁伍马盆地古近系中新统朵体为目标,综合利用三维地震和测井资料,通过地震相和地震属性分析,确定朵体分布及内部岩性特征。在定量恢复重力流流速基础上,结合区域洋流资料,揭示底流与重力流交互作用对朵体沉积的控制作用。结果表明受向北流动的底流影响,早期水道向南迁移,北侧天然堤发育,在水道南侧形成负向地形,导致后期朵体沉积于水道南侧。底流作用下的朵体沉积特征受底流和朵体内重力流流向的影响。当底流与朵体延伸方向相反且斜交时,由于重力流流速远大于底流,朵体在逆底流流向一翼(南侧),重力流与底流流向几乎相反,底流影响被抑制,朵体从轴部向末端逐渐变薄,泥质含量增加;在顺底流流向一翼(北侧),重力流方向与底流斜交,重力流顶部泥质被底流淘洗出去,朵体以砂质沉积为主。当底流与重力流流向接近正交时,朵体受底流影响整体上一致,重力流顶部泥质被淘洗,逆底流流向一翼朵体以厚层砂质沉积为主,向顺底流流向一翼砂体厚度减小、泥质含量增加。

“诗歌并不孤独”:米歇尔·德吉诗歌伦理思想探析

米歇尔·德吉(Michel Deguy)是法国20世纪重要的诗人、哲学家,毕生致力于探寻诗歌伦理意蕴。他的理论体系始终围绕诗歌的“延异”思想展开,关注事物之间的差异与联系。本文首先探析德吉对“诗歌”体裁的理解,然后从三个层面解析德吉的诗歌伦理思想:首先,诗歌是如何“指涉”或“参照”现实世界;其次,“之间”作为德吉思考诗学的思维方式之一该如何被理解;最后,诗歌的“好客性”该如何经由翻译之路被传递下去。研究发现,德吉对诗歌、诗学的思考拉近了事物之间的关系,使文化在差异中相伴共生,包容共存。

Argа-Bilig theory in traditional Mongolian medicine:a theoretical and practical perspective

The concept of Arga and Bilig serves as a foundational principle in both ancient Mongolian philosophy and traditional Mongolian medicine (TMM). Arga, symbolized by brightness and associated with qualities of fire and activity, complements Bilig, symbolized by darkness and representing attributes of water and stillness. Together, these opposing forces permeate all aspects of existence, from the genesis of parenthood to the interplay of day and night. Understanding Arga-Bilig is crucial for diagnosing and treating diseases, as it illuminates the source of imbalance within the body. This review provides an overview of the significance of Arga-Bilig in Mongolian philosophy and its application in TMM, emphasizing the dynamic interplay of these opposing forces and their role in maintaining balance and harmony within the body.

Effects of interplay between metal subwavelength slits on extraordinary optical transmission

In this paper we study the extraordinary optical transmission of one-dimensional multi-slits in an ideal metal film.The transmissivity is calculated as a function of various structural parameters.The transmissivity oscillates,with the period being just the light wavelength,as a function of the spacing between slits.As the number of slits increases,the transmissivity varies in one of three ways.It can increase,attenuate,or remain basically unchanged,depending on the spacing between slits.Each way is in an oscillatory manner.The slit interaction responsible for the oscillating transmission strength that depends on slit spacing is the subject of more detailed investigation.The interaction most intuitively manifests as a current distribution in the metal surface between slits.We find that this current is attenuated in an oscillating fashion from the slit corners to the center of the region between two adjacent slits,and we present a mathematical expression for its waveform.

BMP-IHH-mediated interplay between mesenchymal stem cells and osteoclasts supports calvarial bone homeostasis and repair

Calvarial bones are connected by fibrous sutures. These sutures provide a niche environment that includes mesenchymal stem cells(MSCs), osteoblasts, and osteoclasts, which help maintain calvarial bone homeostasis and repair. Abnormal function of osteogenic cells or diminished MSCs within the cranial suture can lead to skull defects, such as craniosynostosis. Despite the important function of each of these cell types within the cranial suture, we have limited knowledge about the role that crosstalk between them may play in regulating calvarial bone homeostasis and injury repair. Here we show that suture MSCs give rise to osteoprogenitors that show active bone morphogenetic protein(BMP) signalling and depend on BMP-mediated Indian hedgehog(IHH) signalling to balance osteogenesis and osteoclastogenesis activity. IHH signalling and receptor activator of nuclear factor kappa-Β ligand(RANKL) may function synergistically to promote the differentiation and resorption activity of osteoclasts. Loss of Bmpr1a in MSCs leads to downregulation of hedgehog(Hh) signalling and diminished cranial sutures. Significantly, activation of Hh signalling partially restores suture morphology in Bmpr1a mutant mice, suggesting the functional importance of BMP-mediated Hh signalling in regulating suture tissue homeostasis. Furthermore, there is an increased number of CD200+ cells in Bmpr1a mutant mice, which may also contribute to the inhibited osteoclast activity in the sutures of mutant mice. Finally, suture MSCs require BMPmediated Hh signalling during the repair of calvarial bone defects after injury. Collectively, our studies reveal the molecular and cellular mechanisms governing cell–cell interactions within the cranial suture that regulate calvarial bone homeostasis and repair.

Interplay of SOX transcription factors and microRNAs in the brain under physiological and pathological conditions

Precise tuning of gene expression,accomplished by regulato ry networks of transcription factors,epigenetic modifiers,and microRNAs,is crucial for the proper neural development and function of the brain cells.The SOX transcription factors are involved in regulating diverse cellular processes during embryonic and adult neurogenesis,such as maintaining the cell stemness,cell prolife ration,cell fate decisions,and terminal diffe rentiation into neurons and glial cells.MicroRNAs represent a class of small non-coding RNAs that play important roles in the regulation of gene expression.Together with other gene regulatory factors,microRNAs regulate different processes during neurogenesis and orchestrate the spatial and temporal expression important for neurodevelopment.The emerging data point to a complex regulatory network between SOX transcription factors and microRNAs that govern distinct cellular activities in the developing and adult brain.Deregulated SOX/mic roRNA interplay in signaling pathways that influence the homeostasis and plasticity in the brain has been revealed in various brain pathologies,including neurodegenerative disorders,traumatic brain injury,and cancer.Therapeutic strategies that target SOX/microRNA interplay have emerged in recent years as a promising tool to target neural tissue regeneration and enhance neuro restoration.N umerous studies have confirmed complex intera ctions between microRNAs and SOX-specific mRNAs regulating key features of glioblastoma.Keeping in mind the crucial roles of SOX genes and microRNAs in neural development,we focus this review on SOX/microRNAs interplay in the brain during development and adulthood in physiological and pathological conditions.Special focus was made on their interplay in brain pathologies to summarize current knowledge and highlight potential future development of molecular therapies.

Avid Interplay在后期制作及管理中的作用

当今的媒体行业正在经历前所未有的变革。从内容创建、发布到后续流程,媒体价值链的每个方面都受到了影响。为了能够在这样一个风云变幻的时代生存下去,媒体机构必须加强内部团队之间的协作,最大限度地提升自身资产的价值,制作更优质的节目,吸引各种渠道的受众。本文介绍了Avid Interplay在后期制作及网络管理中的作用。

SFC placement and dynamic resource allocation based on VNF performance-resource function and service requirement in cloud-edge environment

With the continuous development of network func-tions virtualization(NFV)and software-defined networking(SDN)technologies and the explosive growth of network traffic,the requirement for computing resources in the network has risen sharply.Due to the high cost of edge computing resources,coordinating the cloud and edge computing resources to improve the utilization efficiency of edge computing resources is still a considerable challenge.In this paper,we focus on optimiz-ing the placement of network services in cloud-edge environ-ments to maximize the efficiency.It is first proved that,in cloud-edge environments,placing one service function chain(SFC)integrally in the cloud or at the edge can improve the utilization efficiency of edge resources.Then a virtual network function(VNF)performance-resource(P-R)function is proposed to repre-sent the relationship between the VNF instance computing per-formance and the allocated computing resource.To select the SFCs that are most suitable to deploy at the edge,a VNF place-ment and resource allocation model is built to configure each VNF with its particular P-R function.Moreover,a heuristic recur-sive algorithm is designed called the recursive algorithm for max edge throughput(RMET)to solve the model.Through simula-tions on two scenarios,it is verified that RMET can improve the utilization efficiency of edge computing resources.

Interplay——MDK2

具有第一人称3D射击色彩的动作类型游戏,除了基本的跑、跳和射击操作外,三位主角还各有独家密招可以使用,在动作元素外还加入了不少解谜冒险因素。

水稻次生代谢物质介导红腹缢管蚜与白背飞虱的种间互作关系

【目的】探究白背飞虱Sogatella furcifera与红腹缢管蚜Rhopalosiphum rufiabdominalis种间互作机制,以及2种昆虫互作过程中水稻次生代谢物质的作用。【方法】将白背飞虱和红腹缢管蚜按不同比例混合饲养,待白背飞虱长至成虫后分组配对,分析白背飞虱总产卵量及日均产卵量;测定各处理的水稻草酸、黄酮及总酚含量,利用GC/MS仪器分析各处理水稻幼苗次生代谢物质成分的差异。【结果】“15头蚜虫+5头白背飞虱”处理的白背飞虱总产卵量仅为131.67粒,与“20头白背飞虱”处理(214.60粒)差异显著,日均产卵量也呈现相同的规律;“15头蚜虫+5头白背飞虱”处理的水稻黄酮和总酚含量分别为1.98和63.71 mg/L,均显著高于其他处理。GC/MS分析表明,“15头蚜虫+5头白背飞虱”处理的水稻中丙丁酚相对含量高达75.78%,而其余处理的水稻中不存在此种酚类物质。【结论】红腹缢管蚜可能是通过刺激水稻提高黄酮及总酚含量,进而抑制白背飞虱的生殖能力,其中丙丁酚可能在白背飞虱和红腹缢管蚜种间互作中起着关键作用。

Cloud-edge-based We-Market:Autonomous Bidding and Peer-to-peer Energy Sharing Among Prosumers

With the extensive penetration of distributed renewable energy and self-interested prosumers,the emerging power market tends to enable user autonomy by bottom-up control and distributed coordination.This paper is devoted to solving the specific problems of distributed energy management and autonomous bidding and peer-to-peer(P2P)energy sharing among prosumers.A novel cloud-edge-based We-Market is presented,where the prosumers,as edge nodes with independent control,balance the electricity cost and thermal comfort by formulating a dynamic household energy management system(HEMS).Meanwhile,the autonomous bidding is initiated by prosumers via the modified Stone-Geary utility function.In the cloud center,a distributed convergence bidding(CB)algorithm based on consistency criterion is developed,which promotes faster and fairer bidding through the interactive iteration with the edge nodes.Besides,the proposed scheme is built on top of the commercial cloud platform with sufficiently secure and scalable computing capacity.Numerical results show the effectiveness and practicability of the proposed We-Market,which achieves 15%cost reduction with shorter running time.Comparative analysis indicates better scalability,which is more suitable for largerscale We-Market implementation.