Residual attention-based tracking-by-detection network with attention-driven data augmentation

Tracking-by-detection (TBD) is a significant framework for visual object tracking. However, current trackers are usually updated online based on random sampling with a probability distribution. The performance of the learning-based TBD trackers is limited by the lack of discriminative features, especially when the background is full of semantic distractors. We propose an attention-driven data augmentation method, in which a residual attention mechanism is integrated into the TBD tracking network as supplementary references to identify discriminative image features. A mask generating network is used to simulate changes in target appearances to obtain positive samples, where attention information and image features are combined to identify discriminative features. In addition, we propose a method for mining hard negative samples, which searches for semantic distractors with the response of the attention module. The experiments on the OTB2015, UAV123, and LaSOT benchmarks show that this method achieves competitive performance in terms of accuracy and robustness.     

An Instance-Learning-Based Intrusion-Detection System for Wireless Sensor Networks

This paper proposes an instance?learning?based intrusion?detection system (IL?IDS) for wireless sensor networks (WSNs). The goal of the proposed system is to detect routing attacks on a WSN. Taking an ...     

基于微信公众平台的外语微学习资源设计与应用研究

针对微信使用群体的普及,本文提出在构建外语学习资源信息管理系统的基础上,搭建一个用于学生校园学习的微信服务账号,从而通过该微信服务账号实现学生对学习资源的利用,更好的促进语言能力的提升。本文借助Web服务器、Java技术、数据库、XML解析技术等系统进行开发,并对其实现进行详细的阐述,从而实现信息化技术与教学方式的创新,具有很大的推广和应用价值。     

基于深度学习的大规模电磁信号识别

近年来,很多高质量的数据集支撑了深度学习在计算机视觉、语音和自然语言处理领域的快速发展.但在电磁信号识别领域仍缺乏高质量的数据集,为促进深度学习在电磁信号识别中的应用,本文基于广播式自动相关监视(ADS-B)建立了一个大规模的真实电磁信号数据集.首先设计了一个自动数据收集和标注系统,在开放和真实的场景中自动捕获ADS-...     

多值逻辑学习控制

本文提出一种由多个既关联又独立的子网络构成的用于逼近非线性函数的多值学习网络（ＫＬＮ），并在此基础上，研究了具有自学习功能的多值逻辑控制。网络运算主要为整数的加法和逻辑判断。仿真结果表明，基于ＫＬＮ的多值逻辑控制系统对复杂非线性系统具有良好的学习控制性能，且具有计算简单和省时的特点。     

一种基于Web的分类体系学习算法

领域分类结构的抽取已成为本体工程和本体学习的关键部分,提出一种新的分类结构学习算法,将Web作为知识获取的语料库,运用迭代方法抽取相关语言学模式,再利用语言学模式抽取分类结构,并采用改进的互信息方法对结果进行评价和过滤,最后通过实验对该分类学习算法的性能进行评价.实验表明：算法具有良好的跨领域性,在准确率和召回率方面也有改善.     

基于学科理解的初中化学教师专业发展研究——以大概念课例“物质成分的探究”研发项目为例

学科理解是教师专业发展的重要内容,可以为素养为本的课堂教学提供学科功能分析。收集在大概念课例“物质成分的探究”研讨过程中的研讨视频、教师访谈录音、课堂教学视频、教学设计等数据,利用学科功能分析要素通过定性内容分析法分析在专业学习共同体干预下初中化学教师的学科理解的特征及变化,从而探寻教师的专业发展情况。结果表明,教师的学科理解发生了显著的改变,教师的专业素养在理论和实践上均有转变。同时也证实了专业学习共同体干预下基于学科理解的大概念课例研讨活动可以促进教师的专业发展,为教研活动和教师培训提供新的视角和范式。     

Rakeness with block sparse Bayesian learning for efficient ZigBee‐based EEG telemonitoring

Future healthcare systems are shifted toward long‐term patient monitoring using embedded ultra‐low power devices. In this paper, the strengths of both rakeness‐based compressive sensing (CS) and block sparse Bayesian learning (BSBL) are exploited for efficient electroencephalogram (EEG) transmission/reception over wireless body area networks. A binary sensing matrix based on the rakeness concept is used to find the most energetic signal directions. A balance is achieved between collecting energy and enforcing restricted isometry property to capture the underlying signal structure. Correct presentation of the EEG oscillatory activity, EEG wave shape, and main signal characteristics is provided using the discrete cosine transform based BSBL, which models the intra‐block correlation. The IEEE 802.15.4 wireless communication technology (ZigBee) is employed, since it targets low data rate communications in an energy efficient manner. To alleviate noise and channel multipath effects, a recursive least square based equalizer is used, with an adaptation algorithm that continually updates the filter weights using successive input samples. For the same compression ratio (CR), results indicate that the proposed system permits a higher reconstruction quality compared with the standard CS algorithm. For higher CRs, lower dimensional projections are allowed, meanwhile guaranteeing a correct reconstruction. Thus, low computational high quality data compression/reconstruction are achieved with minimal energy expenditure at the sensors nodes.     

Adaptive quality of service‐based routing approaches: development of neuro‐dynamic state‐dependent reinforcement learning algorithms

In this paper, we propose two adaptive routing algorithms based on reinforcement learning. In the first algorithm, we have used a neural network to approximate the reinforcement signal, allowing the learner to take into account various parameters such as local queue size, for distance estimation. Moreover, each router uses an online learning module to optimize the path in terms of average packet delivery time, by taking into account the waiting queue states of neighbouring routers. In the second algorithm, the exploration of paths is limited to N‐best non‐loop paths in terms of hops number (number of routers in a path), leading to a substantial reduction of convergence time. The performances of the proposed algorithms are evaluated experimentally with OPNET simulator for different levels of traffic's load and compared with standard shortest‐path and Q‐routing algorithms. Our approach proves superior to classical algorithms and is able to route efficiently even when the network load varies in an irregular manner. We also tested our approach on a large network topology to proof its scalability and adaptability. Copyright © 2006 John Wiley & Sons, Ltd.     

基于压缩感知与结构自相似性的遥感图像超分辨率方法

本文提出了一种基于压缩感知、结构自相似性和字典学习的遥感图像超分辨率方法,其基本思路是建立能够稀疏表示原始高分辨率图像块的字典。实现超分辨率所必需的附加信息来源于遥感图像中广泛存在的自相似结构,该信息可在压缩感知框架下通过字典学习而得到。这里,本文采用K-SVD方法构建字典、并采用OMP方法获取用于稀疏表达的相关系数。与现有基于样本的超分辨率方法的最大不同在于,本文方法仅使用了低分辨率图像及其插值图像,而不需要使用其它高分辨率图像。另外,为了评价方法的效果,本文还引入了一个衡量图像结构自相似性程度的新型指标SSSIM。对比实验结果表明,本文方法具有更好的超分辨率重构效果和运算效率,并且SSSIM指标与超分辨率重构效果具有较强的相关性。