An Investigation of College English Autonomous Learning in Network Multimodal Context

In the current society, based on the growing development of network information technology, the teaching in many colleges and universities has also introduced it to adapt to the situation. This trend can provide more useful conditions for students to learn, which requires students to master enough self-learning abilities to adapt to this model. The study in the paper shows that students are usually interested in autonomous learning in a multimodal environment, but the degree of strategy choice is relatively low, and the learning process is blind and passive with the lack of self-confidence. Facing the future, schools should actively integrate into network thinking, and teachers should change their roles and train and guide students’ learning strategies and learning motivations, so as to achieve better teaching results.

An Analysis of the Cognitive Constraints of Parody in the Context of Network Media

The language used in the context of network media features variation, flexibility, and comprehensiveness, in which parody is one of the most employed devices. Modern cognitive science prefers to take parody as a cognitive act rather than a sheer linguistic modification act, which has provided a new prospective for further research about parody in the context of network media. This paper aims to probe into the cognitive constraints in the mechanism of parody in the context of network media, in hope to make a detailed explanation of the cognitive process of it and to strengthen the comprehension and appreciation of it.

Context Aware Wireless Sensor Network Suitable for Precision Agriculture

Using the Wireless Sensor Networks WSNs in a wide variety of applications is currently considered one of the most challenging solutions. For instance, this technology has evolved the agriculture field, with the precision agriculture challenge. In fact, the cost of sensors and communication infrastructure continuously trend down as long as the technological advances. So, more growers dare to implement WSN for their crops. This technology has drawn substantial interests by improving agriculture productivity. The idea consists of deploying a number of sensors in a given agricultural parcel in order to monitor the land and crop conditions. These readings help the farmer to make the right inputs at the right moment. In this paper, we propose a complete solution for gathering different type of data from variable fields of a large agricultural parcel. In fact, with the in-field variability, adopting a unique data gathering solution for all kinds of fields reveals an inconvenient approach. Besides, as a fault-tolerant application, precision agriculture does not require a high precision value of sensed data. So, our approach deals with a context aware data gathering strategy. In other words, depending on a defined context for the monitored field, the data collector will decide the data gathering strategy to follow. We prove that this approach improves considerably the lifetime of the application.

A Survey on Context-Aware Sensing for Body Sensor Networks

Context awareness in Body Sensor Networks (BSNs) has the significance of associating physiological user activity and the environment to the sensed signals of the user. The context information derived from a BSN can be used in pervasive healthcare monitoring for relating importance to events and specifically for accurate episode detection. In this paper, we address the issue of context-aware sensing in BSNs, and survey different techniques for deducing context awareness.

CAND-IDS: A Novel Context Aware Intrusion Detection System in Cooperative Wireless Sensor Networks by Nodal Node Deployment

Cooperative wireless sensor networks have drastically grown due to node co-opera- tive in unaltered environment. Various real time applications are developed and deployed under cooperative network, which controls and coordinates the flow to and from the nodes to the base station. Though nodes are interlinked to give expected state behavior, it is vital to monitor the malicious activities in the network. There is a high end probability to compromise the node behavior that leads to catastrophes. To overcome this issue a Novel Context Aware-IDS approach named Context Aware Nodal Deployment-IDS (CAND-IDS) is framed. During data transmission based on node properties and behavior CAND-IDS detects and eliminates the malicious nodes in the explored path. Also during network deployment and enhancement, node has to follow Context Aware Cooperative Routing Protocol (CCRP), to ensure the reliability of the network. CAND-IDS are programmed and simulated using Network Simulator software and the performance is verified and evaluated. The simulation result shows significant improvements in the throughput, energy consumption and delay made when compared with the existing system.

Context-Aware System Modeling Based on Boolean Control Network

Boolean control network consists of a set of Boolean variables whose state is determined by other variables in the network. Boolean network is used for modeling complex system. In this paper, we have presented a model of a context-aware system used in smart home based on Boolean control networks. This modeling describes the relationship between the context elements (person, time, location, and activity) and services (Morning Call, Sleeping, Guarding, Entertainment, and normal), which is effective to logical inference. We apply semi tensor matrix product to describe the dynamic of the system. This matrix form of expression is a convenient and reasonable way to design logic control system.

基于网络的Context Aware智能控制系统

提出一种基于网络的Context Aware智能控制系统的体系结构、Context任务控制管理模型。系统将机器人作为Context Aware智能系统的智能接口。通过对Java JINI的服务查找机制进行修改,增强了JINI的服务查找、分发机制,使得系统可以通过综合分析服务提供者当前状态、服务申请者的状态、系统当前的性能以及Context感知信息,经模糊决策,通过”Pull”方式,或“Push”方式给服务申请者(或第三方用户)提供所需的服务。最后对系统性能进行分析。一个应用实例说明Context Aware智能控制系统的应用。

Analysis of the Features of Network Words

The information society makes people's lives gradually enter a digital state for living. And the popularity of the Internet has led to the unique phenomenon of network words. What impact will network and the combination of language bring about? This article will explore the relation between the phenomenon of network words and social context from the angle of social linguistic through the analysis of network words and grammatical features.

Application-Transparent Live Migration for Virtual Machine on Network Security Enhanced Hypervisor

As the number of Virtual Machines(VMs) consolidated on single physical server increases with the rapid advance of server hardware,virtual network turns complex and frangible.Modern Network Security Engines(NSE) are introduced to eradicate the intrusions occurring in the virtual network.In this paper,we point out the inadequacy of the present live migration implementation,which hinders itself from providing transparent VM relocation between hypervisors equipped with Network Security Engines(NSE-H).This occurs because the current implementation ignores VM-related Security Context(SC) required by NSEs embedded in NSE-H.We present the CoM,a comprehensive live migration framework,for NSE-H-based virtualization computing environment.We built a prototype system on Xen hypervisors to evaluate our framework,and conduct experiments under various realistic application environments.The results demonstrate that our solution successfully fixes the inadequacy of the present live migration implementation,and the performance overhead is negligible.

Trusted Routing Based on Identity-Based Threshold Signature for Opportunistic Networks

In opportunistic Networks,compromised nodes can attack social context-based routing protocols by publishing false social attributes information.To solve this problem,we propose a security scheme based on the identity-based threshold signature which allows mobile nodes to jointly generate and distribute the secrets for social attributes in a totally self-organized way without the need of any centralized authority.New joining nodes can reconstruct their own social attribute signatures by getting enough partial signature services from encounter opportunities with the initial nodes.Mobile nodes need to testify whether the neighbors can provide valid attribute signatures for their routing advertisements in order to resist potential routing attacks.Simulation results show that:by implementing our security scheme,the network delivery probability of the social context-based routing protocol can be effectively improved when there are large numbers of compromised nodes in opportunistic networks.

Intelligent Silent Zone for Source-Location Privacy Based on Context-Awareness in WSNs

In many wireless sensor networks(WSNs)applications,the preservation of source-location privacy plays a critical role in concealing context information,otherwise the monitored entities or subjects may be put in danger.Many traditional solutions have been proposed based on the creation of random routes,such as random walk and fake sources approach,which will lead to serious packet delay and high energy consumption.Instead of applying the routing in a blind way,this article proposes a novel solution for source location privacy in WSNs by utilizing sensor ability of perceiving the presence a mobile attacker nearby,for patient attackers in particular to increase the safety period and decrease the data delivery delay.The proposed strategy forms an intelligent silent zone(ISZ)by sacrificing only a minority of sensor nodes to entice patient attackers away from real packet routing path.The analysis and simulation results show that the proposed scheme,besides providing source location privacy energy efficiently,can significantly reduce real event reporting latency compared with the existing approaches.

Nonlinear Prediction with Deep Recurrent Neural Networks for Non-Blind Audio Bandwidth Extension

Non-blind audio bandwidth extension is a standard technique within contemporary audio codecs to efficiently code audio signals at low bitrates. In existing methods, in most cases high frequencies signal is usually generated by a duplication of the corresponding low frequencies and some parameters of high frequencies. However, the perception quality of coding will significantly degrade if the correlation between high frequencies and low frequencies becomes weak. In this paper, we quantitatively analyse the correlation via computing mutual information value. The analysis results show the correlation also exists in low frequency signal of the context dependent frames besides the current frame. In order to improve the perception quality of coding, we propose a novel method of high frequency coarse spectrum generation to improve the conventional replication method. In the proposed method, the coarse high frequency spectrums are generated by a nonlinear mapping model using deep recurrent neural network. The experiments confirm that the proposed method shows better performance than the reference methods.

On the QoS Assessment of Video Sessions in Heterogeneous 3G-WLAN Networks with Seamless and Secure Mobility Support

Real-time video data transmission is currently emerging as a popular application among mobile users but it is very sensitive to QoS degradation due to packet losses in wireless networks.In order to achieve service continuity and integrity upon handoffs among heterogeneous networks,provisioning of seamless and secure mobility is required.However,in order to reduce the delay and packet losses during vertical handovers we need to employ supportive protocols like context transfer.In this paper we evaluate the QoS of video transmission over a heterogeneous 3G-WLAN network.The aggregate video data traffic is represented by a dynamic two-dimensional Markov chain model,which has been evaluated against real video data measurement.Upon the vertical handover, appropriate AAA handshaking and enhanced mobility management using context transfer have been considered.Perceived QoS evaluation of video streams was performed based on peak signal-noise ratio(PSNR) measurements,while we analyticallyestimated the number of packet losses during handovers.The results show that both packet loss within the converged network and loss occurrence affecting the perceived video quality is reduced. Moreover,the proposed context transfer scheme minimizes handover delay and the number of lost packet up to 3 times compared to standard AAA handshaking.

Disclosure and Use of Privacy Settings in Facebook^TMProfiles: Evaluating the Impact of Media Context and Gender

The present study examined disclosure and use of privacy settings in online social networking profiles as a function of the media context (i.e., online versus hard copy (paper and pencil) FacebookTM profiles). Gender was also examined. Overall, participants disclosed more information when constructing a profile for another person when using a hard copy paper and pencil format than an online context. Gender differences were not uniform across media contexts, however, in contrast to traditional disclosure theory, females censored their disclosures more so than males but only for some topics. Only 20% of the sample increased their use of privacy settings. Consistent with patterns of disclosure, descriptive comparison suggests that more settings were employed in the paper and pencil than online context and more privacy settings were employed by females.

轻量级重参数化的遥感图像超分辨率重建网络设计

针对当前基于深度学习的遥感图像超分辨率重建模型部署时对硬件要求较高,本文设计了一种轻量级基于重参数化的残差特征遥感图像超分辨率重建网络。首先,采用重参数化方法设计了一种残差局部特征模块,以有效地提取图像局部特征;同时考虑到图像内部出现的相似特征,设计了一个轻量级的全局上下文模块对图像的相似特征进行关联以提升网络的特征表达能力,并通过调整该模块的通道压缩倍数来减少模型的参数量和改善模型的性能;最后,在上采样模块前使用多层特征融合模块聚合所有的深度特征,以产生更全面的特征表示。在UC Merced遥感数据集上进行测试,该算法在遥感图像3倍超分辨率下的参数量为539 K,峰值信噪比为30.01 dB,结构相似性为0.8449,模型的推理时间为0.010 s;而HSENet算法的参数量为5470 K,峰值信噪比为30.00 dB,结构相似性为0.8420,模型的推理时间为0.059 s。实验结果表明,该算法相比HSENet算法,参数量更少,运行速度较快,且峰值信噪比与结构相似性也有一定的提高。在DIV2K自然图像数据集上进行测试,该算法的峰值信噪比和结构相似性相比其他算法也有一定的优势,表明该算法的泛化能力较强。

A Design of Incremental Granular Network for Software Data Modeling

In this paper, we propose an incremental method of Granular Networks (GN) to construct conceptual and computational platform of Granular Computing (GrC). The essence of this network is to describe the associations between information granules including fuzzy sets formed both in the input and output spaces. The context within which such relationships are being formed is established by the system developer. Here information granules are built using Context-driven Fuzzy Clustering (CFC). This clustering develops clusters by preserving the homogeneity of the clustered patterns associated with the input and output space. The experimental results on well-known software module of Medical Imaging System (MIS) revealed that the incremental granular network showed a good performance in comparison to other previous literature.

基于上下文编码器网络的油气曲线图像脱敏方法研究

数据对于石油企业就是资产。数据加密与数据脱敏都是针对数据安全的策略,但目前数据脱敏技术仅能够针对文本实现,缺少石油企业中大量保有的图像数据脱敏技术。针对目前脱敏技术存在的不足之处及油气田勘探中的曲线图像数据特点,借鉴深度学习图像修复方法,提出一套基于上下文编码器的脱敏技术,弥补了行业内对于图像数据无法脱敏的缺陷。对该方法进行详细的测试后取得了较好的脱敏效果。对比传统方法,结果证明其能够在曲线图像这种具有专业特殊性的数据中取得更明显的效果以及更可靠的脱敏效果。

脑电情感识别中多上下文向量优化的卷积递归神经网络

目前的脑电(EEG)情感识别模型忽略了不同时段情感状态的差异性,未能强化关键的情感信息。针对上述问题,提出一种多上下文向量优化的卷积递归神经网络(CR-MCV)。首先构造脑电信号的特征矩阵序列,通过卷积神经网络(CNN)学习多通道脑电的空间特征;然后利用基于多头注意力的递归神经网络生成多上下文向量进行高层抽象特征提取;最后利用全连接层进行情感分类。在DEAP(Database for Emotion Analysis using Physiological signals)数据集上进行实验,CR-MCV在唤醒和效价维度上分类准确率分别为88.09%和89.30%。实验结果表明,CR-MCV在利用电极空间位置信息和不同时段情感状态显著性特征基础上,能够自适应地分配特征的注意力并强化情感状态显著性信息。

用于多器官分割的多尺度聚合网络研究

多器官分割在病理分析、手术方案制定以及临床诊断上都具有重要的应用价值.但是,一些器官形变较大、尺寸较小且组织边缘模糊,导致分割效果较差.为了解决该问题,提出了一种用于多器官分割的多尺度聚合网络(MSANet).MSANet有两个优势:首先,设计了多尺度边界提取模块,使用多尺度卷积核提取多个特征图,将不同尺度的特征图互相结合,从而聚合全局上下文信息,并提取不同器官的边界和细节信息;其次,设计了聚焦式注意力模块,通过学习的注意力权重来调节特征图的重要性,从而聚焦感兴趣的多器官区域并捕捉不同器官的关键特征,进一步提高分割性能.在两个公开数据集CHAOS和MS-CMRSeg上进行了大量实验.实验结果表明:MSANet在两个数据集上的分割效果均优于当前主流的多器官分割方法,显著提高了多器官分割精度.

集成多种上下文与混合交互的显著性目标检测

显著性目标检测目的是识别和分割图像中的视觉显著性目标,它是计算机视觉任务及其相关领域的重要研究内容之一。当下基于全卷积网络(FCNs)的显著性目标检测方法已经取得了不错的性能,然而现实场景中的显著性目标类型多变且尺寸不固定,这使得准确检测并完整分割出显著性目标仍然是一个巨大的挑战。为此,该文提出集成多种上下文和混合交互的显著性目标检测方法,通过利用密集上下文信息探索模块和多源特征混合交互模块来高效预测显著性目标。密集上下文信息探索模块采用空洞卷积、不对称卷积和密集引导连接渐进地捕获具有强关联性的多尺度和多感受野上下文信息,通过集成这些信息来增强每个初始多层级特征的表达能力。多源特征混合交互模块包含多种特征聚合操作,可以自适应交互来自多层级特征中的互补性信息,以生成用于准确预测显著性图的高质量特征表示。此方法在5个公共数据集上进行了性能测试,实验结果表明,该文方法在不同的评估指标下与19种基于深度学习的显著性目标检测方法相比取得优越的预测性能。