Role-Based Network Embedding via Quantum Walk with Weighted Features Fusion

Role-based network embedding aims to embed role-similar nodes into a similar embedding space,which is widely used in graph mining tasks such as role classification and detection.Roles are sets of nodes in graph networks with similar structural patterns and functions.However,the rolesimilar nodes may be far away or even disconnected from each other.Meanwhile,the neighborhood node features and noise also affect the result of the role-based network embedding,which are also challenges of current network embedding work.In this paper,we propose a Role-based network Embedding via Quantum walk with weighted Features fusion(REQF),which simultaneously considers the influence of global and local role information,node features,and noise.Firstly,we capture the global role information of nodes via quantum walk based on its superposition property which emphasizes the local role information via biased quantum walk.Secondly,we utilize the quantum walkweighted characteristic function to extract and fuse features of nodes and their neighborhood by different distributions which contain role information implicitly.Finally,we leverage the Variational Auto-Encoder(VAE)to reduce the effect of noise.We conduct extensive experiments on seven real-world datasets,and the results show that REQF is more effective at capturing role information in the network,which outperforms the best baseline by up to 14.6% in role classification,and 23% in role detection on average.

Co-design for an SoC embedded network controller

With the development of Ethernet systems and the growing capacity of modern silicon technology, embedded communication networks are playing an increasingly important role in embedded and safety critical systems. Hardware/software co-design is a methodology for solving design problems in processor based embedded systems. In this work, we implemented a new 1-cycle pipeline microprocessor and a fast Ethernet transceiver and established a low cost, high performance embedded network controller, and designed a TCP/IP stack to access the Internet. We discussed the hardware/software architecture in the forepart, and then the whole system-on-a-chip on Altera Stratix EP1S25F780C6 device. Using the FPGA environment and SmartBit tester, we tested the system’s throughput. Our simulation results showed that the maximum throughput of Ethernet packets is up to 7 Mbps, that of UDP packets is up to 5.8 Mbps, and that of TCP packets is up to 3.4 Mbps, which showed that this embedded system can easily transmit basic voice and video signals through Ethernet, and that using only one chip can realize that many electronic devices access to the Internet directly and get high performance.

Varactor-tunable frequency selective surface with an embedded bias network

A new technique for designing a varactor-tunable frequency selective surface （FSS） with an embedded bias network is proposed and experimentally verified. The proposed FSS is based on a square-ring slot FSS. The frequency tuning is achieved by inserting varactor diodes between the square mesh and each unattached square patch. The square mesh is divided into two parts for biasing the varactor diodes. Full-wave numerical simulations show that a wide tuning range can be achieved by changing the capacitances of these loaded varactors. Two homo-type samples using fixed lumped capacitors are fabricated and measured using a standard waveguide measurement setup. Excellent agreement between the measured and simulated results is demonstrated.

Optimum Sizing and Siting of an Embedded Solar Photovoltaic Generation: A Case Study of 33 kv Sub-Transmission Network at Tarkwa, Ghana

The interconnection of Solar PV to the Tarkwa Bulk Supply Point (BSP) has become necessary in order to provide additional capacity to meet the ever-increasing demand of Tarkwa and its environs during the day. The Solar PV Plant will support the Tarkwa BSP during the day. In this study, a grid impact analysis for the integration of Solar PV plant at three points of common coupling (PCC) at Tarkwa Bulk Supply Point’s (BSP) 33 kV network of the Electricity Company of Ghana was carried out. The three PCCs were Tarkwa BSP, Ghana Australia Gold (GAG) Substation and Darmang Substation. Simulations and detailed analysis were carried out with the use of CYME Software (Cyme 8.0 Rev 05). The Solar PV was integrated at varying penetration levels of 9 MWp, 11 MWp, 14 MWp, 16 MWp, 18 MWp, 20 MWp and 23 MWp (representing penetration levels of 40%, 50%, 60%, 70%, 80%, 90% and 100%, respectively) of the 2020 projected light demand of Tarkwa BSP 25.15 MVA network at an average power factor of 0.903. From the study, the optimum capacity of Solar PV power that could be connected is 9 MWp at an optimum inverter power factor of 0.94 lagging, and the GAG Substation was identified as the optimal location. The stiffness ratio at the optimal location was determined as 41.9, a figure which is far greater than the minimum standard value of 5, and gives an indication of very little voltage control problems in the operation of the proposed Solar PV interconnection. The integration of the optimum 9 MW Solar PV Plant to the Tarkwa network represents an additional 12.77% capacity, decreased the technical losses by 7.76%, and increased the voltage profile by 1.97%.

FirmVulSeeker—BERT and Siamese Network-Based Vulnerability Search for Embedded Device Firmware Images

In recent years,with the development of the natural language processing(NLP)technologies,security analyst began to use NLP directly on assembly codes which were disassembled from binary executables in order to examine binary similarity,achieved great progress.However,we found that the existing frameworks often ignored the complex internal structure of instructions and didn’t fully consider the long-term dependencies of instructions.In this paper,we propose firmVulSeeker—a vulnerability search tool for embedded firmware images,based on BERT and Siamese network.It first builds a BERT MLM task to observe and learn the semantics of different instructions in their context in a very large unlabeled binary corpus.Then,a finetune mode based on Siamese network is constructed to guide training and matching semantically similar functions using the knowledge learned from the first stage.Finally,it will use a function embedding generated from the fine-tuned model to search in the targeted corpus and find the most similar function which will be confirmed whether it’s a real vulnerability manually.We evaluate the accuracy,robustness,scalability and vulnerability search capability of firmVulSeeker.Results show that it can greatly improve the accuracy of matching semantically similar functions,and can successfully find more real vulnerabilities in real-world firmware than other tools.

基于多嵌入融合的top-N推荐

异构信息网络(Heterogeneous Information Network, HIN)凭借其丰富的语义信息和结构信息被广泛应用于推荐系统中,虽然取得了很好的推荐效果,但较少考虑局部特征放大、信息交互和多嵌入聚合等问题。针对这些问题,提出了一种新的用于top-N推荐的多嵌入融合推荐(Multi-embedding Fusion Recommendation, MFRec)模型。首先,该模型在用户和项目学习分支中都采用对象上下文表示网络,充分利用上下文信息以放大局部特征,增强相邻节点的交互性;其次,将空洞卷积和空间金字塔池化引入元路径学习分支,以便获取多尺度信息并增强元路径的节点表示;然后,采用多嵌入融合模块以便更好地进行用户、项目以及元路径的嵌入融合,细粒度地进行多嵌入之间的交互学习,并强调了各特征的不同重要性程度;最后,在两个公共推荐系统数据集上进行了实验,结果表明所提模型MFRec优于现有的其他top-N推荐系统模型。

结合改进ShuffleNet-V2和注意力机制的无人机图像自主分类预警框架

为实现灾难事件的无人机(Unmanned Aerial Vehicle,UAV)自主监测和预警,提出了结合逐通道注意力机制和高效卷积神经网络的新架构。考虑到嵌入式平台的资源限制条件,使用轻量级ShuffleNet-V2作为骨干网络,能够对更多信息进行高效编码并尽可能降低网络复杂度。为进一步提高灾难场景分类的准确度,在ShuffleNet-V2网络中结合了挤压-激发(Squeeze-Excitation,SE)模块以实现逐通道注意力机制,显著增强分类网络对重要特征的关注度。通过数据采集和增强技术获得包括12876张图像的UAV航拍灾难事件数据集,对所提方法进行性能评估,并比较所提方法与其他先进模型的性能。结果表明,所提方法取得了99.01%的平均准确度,模型大小仅为5.6 MB,且在UAV机载平台上的处理速度超过10 FPS,能够满足UAV平台自主灾情监测任务的现实需求。

A PSO Based Multi-Domain Virtual Network Embedding Approach

This paper proposed a multi-domain virtual network embedding algorithm based on multi-controller SDN architecture. The local controller first selects candidate substrate nodes for each virtual node in the domain. Then the global controller abstracts substrate network topology based on the candidate nodes and boundary nodes of each domain, and applies Particle Swarm Optimization Algorithm on it to divide virtual network requests. Each local controller then embeds the virtual nodes of the divided single-domain virtual network requests in the domain, and cooperates with other local controllers to embed the inter-domain virtual links. Simulation experimental results show that the proposed algorithm has good performance in reducing embedding cost with good stability and scalability.

Virtual 5G Network Embedding in a Heterogeneous and Multi-Domain Network Infrastructure

The pursuit of the higher performance mobile communications forces the emergence of the fifth generation mobile communication(5G). 5G network, integrating wireless and wired domain, can be qualified for the complex virtual network work oriented to the cross-domain requirement. In this paper, we focus on the multi-domain virtual network embedding in a heterogeneous 5G network infrastructure, which facilitates the resource sharing for diverse-function demands from fixed/mobile end users. We proposed the mathematical ILP model for this problem.And based on the layered-substrate-resource auxiliary graph and an effective six-quadrant service-type-judgment method, 5G embedding demands can be classified accurately to match different user access densities. A collection of novel heuristic algorithms of virtual 5G network embedding are proposed. A great deal of numerical simulation results testified that our algorithm performed better in terms of average blocking rate, routing latency and wireless/wired resource utilization, compared with the benchmark.

Cryptocurrency Transaction Network Embedding From Static and Dynamic Perspectives: An Overview

Cryptocurrency, as a typical application scene of blockchain, has attracted broad interests from both industrial and academic communities. With its rapid development, the cryptocurrency transaction network embedding(CTNE) has become a hot topic. It embeds transaction nodes into low-dimensional feature space while effectively maintaining a network structure,thereby discovering desired patterns demonstrating involved users' normal and abnormal behaviors. Based on a wide investigation into the state-of-the-art CTNE, this survey has made the following efforts: 1) categorizing recent progress of CTNE methods, 2) summarizing the publicly available cryptocurrency transaction network datasets, 3) evaluating several widely-adopted methods to show their performance in several typical evaluation protocols, and 4) discussing the future trends of CTNE. By doing so, it strives to provide a systematic and comprehensive overview of existing CTNE methods from static to dynamic perspectives,thereby promoting further research into this emerging and important field.

Heterogeneous Network Embedding: A Survey

Real-world complex networks are inherently heterogeneous;they have different types of nodes,attributes,and relationships.In recent years,various methods have been proposed to automatically learn how to encode the structural and semantic information contained in heterogeneous information networks(HINs)into low-dimensional embeddings;this task is called heterogeneous network embedding(HNE).Efficient HNE techniques can benefit various HIN-based machine learning tasks such as node classification,recommender systems,and information retrieval.Here,we provide a comprehensive survey of key advancements in the area of HNE.First,we define an encoder-decoder-based HNE model taxonomy.Then,we systematically overview,compare,and summarize various state-of-the-art HNE models and analyze the advantages and disadvantages of various model categories to identify more potentially competitive HNE frameworks.We also summarize the application fields,benchmark datasets,open source tools,andperformance evaluation in theHNEarea.Finally,wediscuss open issues and suggest promising future directions.We anticipate that this survey will provide deep insights into research in the field of HNE.

Multiplex network infomax:Multiplex network embedding via information fusion

For networking of big data applications,an essential issue is how to represent networks in vector space for further mining and analysis tasks,e.g.,node classification,clustering,link prediction,and visualization.Most existing studies on this subject mainly concentrate on monoplex networks considering a single type of relation among nodes.However,numerous real-world networks are naturally composed of multiple layers with different relation types;such a network is called a multiplex network.The majority of existing multiplex network embedding methods either overlook node attributes,resort to node labels for training,or underutilize underlying information shared across multiple layers.In this paper,we propose Multiplex Network Infomax(MNI),an unsupervised embedding framework to represent information of multiple layers into a unified embedding space.To be more specific,we aim to maximize the mutual information between the unified embedding and node embeddings of each layer.On the basis of this framework,we present an unsupervised network embedding method for attributed multiplex networks.Experimental results show that our method achieves competitive performance on not only node-related tasks,such as node classification,clustering,and similarity search,but also a typical edge-related task,i.e.,link prediction,at times even outperforming relevant supervised methods,despite that MNI is fully unsupervised.

A Multi-Interface Remote Monitoring and Control System Architecture Based on Embedded Server

This paper presents a multi-interface embedded server architecture for remote real-time monitoring system and distributed monitoring applications. In the scheme,an embedded microprocessor( LPC3250 from NXP) is chosen as the CPU of the embedded server with a linux operation system( OS) environment. The embedded server provides multiple interfaces for supporting various application scenarios. The whole network is based on local area network and adopts the Browser / Server( B / S) model. The monitoring and control node is as a browser endpoint and the remote node with an embedded server is as a server endpoint. Users can easily acquire various sensors information through writing Internet protocol address of remote node on the computer browser. Compared with client / server( C / S) mode,B / S model needs less maintain and can be applicable to large user group. In addition,a simple network management protocol( SNMP) is used for management of devices in Internet protocol( IP) networks. The results of the demonstration experiment show that the proposed system gives good support to manage the network from different user terminals and allows the users to better interact with the ambient environment.

基于Ghost-TiFPN的轻量化快速目标跟踪算法

针对传统孪生目标跟踪算法体量大、难以在嵌入式设备部署以及其在目标尺度变化大、有相似物干扰等条件下效果不佳的问题,提出一种新的轻量化快速跟踪(Ghost fast Tracking with TiFPN and Retriever,GTtracker)算法。引入Ghost机制,对Resnet网络进行重新设计,构建一种轻量化G-Resnet网络对跟踪目标进行快速特征提取。设计轻量自适应加权融合(Tiny adaptive weighted fusion algorithm Feature Pyramid Network,TiFPN)算法,进一步加强特征信息的融合,解决相似物干扰问题。设计一种轻量化区域回归网络(Ghost Decoupled Net,GDnet),以实现目标分类、交并比(Intersection-over-Union,IoU)计算以及边界框回归,并在跟踪阶段应用一种新的目标寻回器提升算法跟踪的成功率。在OTB100数据集和VOT2020数据集上进行算法验证,并移植算法到嵌入式设备Jetson Xavier NX上进行性能测试。实验结果均表明算法的有效性和优越性,相比经典孪生目标跟踪(SiamCAR)算法,新方法在精度和期望平均重叠率(Expected Average Overlap,EAO)指标均相似的前提下,能够实现更快的运行速度,可在Jetson Xavier NX上实时运行,达到30帧/s,且能有效解决相似物干扰、尺度变化大等问题。

Vertex centrality of complex networks based on joint nonnegative matrix factorization and graph embedding

Finding crucial vertices is a key problem for improving the reliability and ensuring the effective operation of networks,solved by approaches based on multiple attribute decision that suffer from ignoring the correlation among each attribute or the heterogeneity between attribute and structure. To overcome these problems, a novel vertex centrality approach, called VCJG, is proposed based on joint nonnegative matrix factorization and graph embedding. The potential attributes with linearly independent and the structure information are captured automatically in light of nonnegative matrix factorization for factorizing the weighted adjacent matrix and the structure matrix, which is generated by graph embedding. And the smoothness strategy is applied to eliminate the heterogeneity between attributes and structure by joint nonnegative matrix factorization. Then VCJG integrates the above steps to formulate an overall objective function, and obtain the ultimately potential attributes fused the structure information of network through optimizing the objective function. Finally, the attributes are combined with neighborhood rules to evaluate vertex's importance. Through comparative analyses with experiments on nine real-world networks, we demonstrate that the proposed approach outperforms nine state-of-the-art algorithms for identification of vital vertices with respect to correlation, monotonicity and accuracy of top-10 vertices ranking.

Deep Learning and Entity Embedding-Based Intrusion Detection Model for Wireless Sensor Networks

Wireless sensor networks(WSNs)are considered promising for applications such as military surveillance and healthcare.The security of these networks must be ensured in order to have reliable applications.Securing such networks requires more attention,as they typically implement no dedicated security appliance.In addition,the sensors have limited computing resources and power and storage,which makes WSNs vulnerable to various attacks,especially denial of service(DoS).The main types of DoS attacks against WSNs are blackhole,grayhole,flooding,and scheduling.There are two primary techniques to build an intrusion detection system(IDS):signature-based and data-driven-based.This study uses the data-driven approach since the signature-based method fails to detect a zero-day attack.Several publications have proposed data-driven approaches to protect WSNs against such attacks.These approaches are based on either the traditional machine learning(ML)method or a deep learning model.The fundamental limitations of these methods include the use of raw features to build an intrusion detection model,which can result in low detection accuracy.This study implements entity embedding to transform the raw features to a more robust representation that can enable more precise detection and demonstrates how the proposed method can outperform state-of-the-art solutions in terms of recognition accuracy.

Combined Linear Multi-Model for Reliable Route Recommender in Next Generation Network

Network analysis is a promisingfield in the area of network applications as different types of traffic grow enormously and exponentially.Reliable route prediction is a challenging task in the Large Scale Networks(LSN).Various non-self-learning and self-learning approaches have been adopted to predict reliable routing.Routing protocols decide how to send all the packets from source to the destination addresses across the network through their IP.In the current era,dynamic protocols are preferred as they network self-learning internally using an algorithm and may not entail being updated physically more than the static protocols.A novel method named Reliable Route Prediction Model(RRPM)is proposed tofind the best routes in the given hefty gage network to balance the load of the entire network to advance the network recital.The task is carried out in two phases.In thefirst phase,Network Embedding(NE)based node classification is carried out.The second phase involves the network analysis to predict the route of the LSN.The experiment is carried out for average data transmission and rerouting time is measured between RRPM and Routing Information Protocol(RIP)protocol models with before and after failure links.It was observed that average transmission time for RIP protocol has measured as 18.5 ms and RRPM protocol has measured as 18.2 ms.Hence the proposed RRPM model outperforms well than the traditional routefinding protocols such as RIP and Open Shortest Path First(OSPF).

Aspect-Based Sentiment Classification Using Deep Learning and Hybrid of Word Embedding and Contextual Position

Aspect-based sentiment analysis aims to detect and classify the sentiment polarities as negative,positive,or neutral while associating them with their identified aspects from the corresponding context.In this regard,prior methodologies widely utilize either word embedding or tree-based rep-resentations.Meanwhile,the separate use of those deep features such as word embedding and tree-based dependencies has become a significant cause of information loss.Generally,word embedding preserves the syntactic and semantic relations between a couple of terms lying in a sentence.Besides,the tree-based structure conserves the grammatical and logical dependencies of context.In addition,the sentence-oriented word position describes a critical factor that influences the contextual information of a targeted sentence.Therefore,knowledge of the position-oriented information of words in a sentence has been considered significant.In this study,we propose to use word embedding,tree-based representation,and contextual position information in combination to evaluate whether their combination will improve the result’s effectiveness or not.In the meantime,their joint utilization enhances the accurate identification and extraction of targeted aspect terms,which also influences their classification process.In this research paper,we propose a method named Attention Based Multi-Channel Convolutional Neural Net-work(Att-MC-CNN)that jointly utilizes these three deep features such as word embedding with tree-based structure and contextual position informa-tion.These three parameters deliver to Multi-Channel Convolutional Neural Network(MC-CNN)that identifies and extracts the potential terms and classifies their polarities.In addition,these terms have been further filtered with the attention mechanism,which determines the most significant words.The empirical analysis proves the proposed approach’s effectiveness compared to existing techniques when evaluated on standard datasets.The experimental results represent our approach outperforms in the F1 measure with an overall achievement of 94%in identifying aspects and 92%in the task of sentiment classification.

A Topology-Cognitive Algorithm Framework for Virtual Network Embedding Problem

The virtual network embedding/mapping problem is an important issue in network virtualization in Software-Defined Networking(SDN).It is mainly concerned with mapping virtual network requests,which could be a set of SDN flows,onto a shared substrate network automatically and efficiently.Previous researches mainly focus on developing heuristic algorithms for general topology virtual network.In practice however,the virtual network is usually generated with specific topology for specific purpose.Thus,it is a challenge to optimize the heuristic algorithms with these topology information.In order to deal with this problem,we propose a topology-cognitive algorithm framework,which is composed of a guiding principle for topology algorithm developing and a compound algorithm.The compound algorithm is composed of several subalgorithms,which are optimized for specific topologies.We develop star,tree,and ring topology algorithms as examples,other subalgorithms can be easily achieved following the same framework.The simulation results show that the topology-cognitive algorithm framework is effective in developing new topology algorithms,and the developed compound algorithm greatly enhances the performance of the Revenue/Cost(R/C) ratio and the Runtime than traditional heuristic algorithms for multi-topology virtual network embedding problem.

Robust Virtual Network Embedding Based on Component Connectivity in Large-Scale Network

Virtual network embedding problem which is NP-hard is a key issue for implementing software-defined network which is brought about by network virtualization. Compared with other studies which focus on designing heuristic algorithms to reduce the hardness of the NP-hard problem we propose a robust VNE algorithm based on component connectivity in large-scale network. We distinguish the different components and embed VN requests onto them respectively. And k-core is applied to identify different VN topologies so that the VN request can be embedded onto its corresponding component. On the other hand, load balancing is also considered in this paper. It could avoid blocked or bottlenecked area of substrate network. Simulation experiments show that compared with other algorithms in large-scale network, acceptance ratio, average revenue and robustness can be obviously improved by our algorithm and average cost can be reduced. It also shows the relationship between the component connectivity including giant component and small components and the performance metrics.