WavNet — Visual saliency detection using Discrete Wavelet Convolutional Neural Network

In the recent advancements in image and video analysis, the detection of salient regions in the image becomes the initial step. This plays a crucial role in deciding the performance of such algorithms. In this work, a Multi-Resolution Feature Extraction (MRFE) technique that makes use of Discrete Wavelet Convolutional Neural Network (DWCNN) for generating features is employed. An Enhanced Feature Extraction (EFE) module extracts additional features from the high level features of the DWCNN, which are used to frame both channel as well as spatial attention models for yielding contextual attention maps. A new hybrid loss function is also proposed, which is a combination of Balanced Cross Entropy (BCE) loss and Edge based Structural Similarity (ESSIM) loss that effectively identifies and segments the salient regions with clear boundaries. The method is tested exhaustively with five different benchmark datasets and is proved superior to the existing state-of-the-art methods with a minimum Mean Absolute error (MAE) of 0.03 and F-measure of 0.956.     

格点QCD基础求解器及其异构计算实现的性能优化

格点量子色动力学(格点QCD)是研究夸克、胶子等微观粒子间相互作用的重要理论和方法. 通过将时空离散化为四维结构网格, 并将量子色动力学的基本场量定义在网格上, 让研究人员可以使用数值模拟方法, 从第一性原理出发研究强子间相互作用和性质, 但这个过程中的计算量极大, 需要进行大规模并行计算. 格点QCD计算的核心基础为格点QCD求解器, 是程序运行主要的计算热点模块. 本文研究在国产异构计算平台下格点QCD求解器的实现与优化, 提出一套格点QCD求解器的设计实现, 实现了BiCGSTAB求解器, 显著降低了迭代次数; 通过对奇偶预处理技术, 降低了所求问题的计算规模; 针对国产异构加速卡的特点, 优化了Dslash模块的访存操作. 实验测试表明, 相比优化前的求解器获得了约30倍的加速比, 为国产异构超算下格点QCD软件性能优化提供了有益的参考价值.     

A multilayer recognition model for twitter user geolocation

Wireless Networks - Geolocation is important for many emerging applications such as disaster management and recommendation system. In this paper, we propose a multilayer recognition model (MRM) to...     

基于改进CGANs的入侵检测方法研究

近年来,机器学习算法在入侵检测系统(IDS)中的应用获得越来越多的关注。然而,传统的机器学习算法更多的依赖于已知样本,因此需要尽可能多的数据样本来对模型进行训练。遗憾地是,随着越来越多未知攻击的出现,且用于训练的攻击样本具有不平衡性,传统的机器学习模型会遇到瓶颈。文章提出一种将改进后的条件生成对抗网络(CGANs)与深度神经网络(DNN)相结合的入侵检测模型(CGANs-DNN),通过解决样本不平衡性问题来提高检测模型对未知攻击类型或只有少数攻击样本类型的检测率。深度神经网络(DNN)具有表征数据潜在特征的能力,而经过改进后的条件CGANs,能够通过学习已知攻击样本潜在数据特征分布,来根据指定类型生成新的攻击样本。此外,与生成对抗网络(GANs)和变分自编码器(VAE)等无监督生成模型相比,CGANsDNN经过改进后加入梯度惩罚项,在训练的稳定性上有了很大地提升。通过NSL-KDD数据集对模型进行评估,与传统算法相比CGANs-DNN不仅在整体准确率、召回率和误报率等方面有更好的性能,而且对未知攻击和只有少数样本的攻击类型具有较高的检测率。     

高效的基于段模式的恶意URL检测方法

提出一种高效的基于段模式的检测恶意URL的方法,该方法首先解析已标注的恶意URL中的域名、路径名和文件名3个语义段,然后通过建立以三元组为词项的倒排索引快速计算恶意URL每个语义段的模式,最后基于倒排索引查找到的段模式来判定给定的URL是否是恶意URL。不仅如此,该方法还支持基于Jaccard的随机域名识别技术来判定包含随机域名的恶意URL。实验结果表明,与当前先进的基准方法相比,该方法具有较好的性能和可扩展性。     

模型驱动的大数据流水线框架PiFlow

复杂流程的大数据处理多依托于流水线系统，但大数据处理的流水线系统在易用性、功能复用性、扩展性以及处理性能等方面存在不足。针对上述问题，为提高大数据处理环境的构建与开发效率,优化处理流程，提出了一种模型驱动的大数据流水线框架PiFlow。首先，将大数据处理过程抽象为有向无环图；然后，开发一系列组件用于构建数据处理流水线，并设计了流水线任务执行机制。同时，为规范和简化流水线框架的描述，设计了基于模型驱动的大数据流水线描述语言——PiFlowDL，该语言以模块化、层次化的方式对大数据处理任务进行描述。PiFlow以所见即所得(WYSIWYG)的方式配置流水线，集成了状态监控、模板配置、组件集成等功能，与Apache NiFi相比有2~7倍的性能提升。     

中国互联网发展的历史阶段划分

首先根据互联网重点应用方向的变迁，简单地把中国互联网的发展历程分为3个阶段．然后详细分析每个阶段的时间跨度、划分依据、阶段特点，最后对互联网未来发展趋势进行预测.     

Leader-following consensus for a class of second-order nonlinear multi-agent systems

This paper deals with the leader-following consensus problem for a class of multi-agent systems with nonlinear dynamics and directed communication topology. The control input of the leader agent is assumed to be unknown to all follower agents. A distributed adaptive nonlinear control law is constructed using the relative state information between neighboring agents, which achieves leader-following consensus for any directed communication graph that contains a spanning tree with the root node being the leader agent. Compared with previous results, the nonlinear functions are not required to satisfy the globally Lipschitz or Lipschitz-like condition and the adaptive consensus protocol is in a distributed fashion. A numerical example is given to verify our proposed protocol.     

Phishing attacks discovery based on HTML layout similarity

Based on the similarity of the layout structure between the phishing sites and real sites,an approach to discover phishing sites was presented.First,the tag with link attribute as a feature was extracted,and then based on the feature,the page tag sequence branch to identify website was extracted,followed by the page layout similarity-HTMLTagAntiPhish,the alignment of page tag sequence tree into the alignment of page tag sequence branches was converted,this converted two-dimention tree structure into one-dimention string structure,and finally through the substitution matrix of bioinfor-matics BLOSUM62 coding,alignment score quickly to improve the phishing sites detection efficiency was computed.A series of simulation experiments show that this approach is feasible and has higher precision and recall rates.     

Zero-Knowledge Argument for Simultaneous Discrete Logarithms

In Crypto 1992, Chaum and Pedersen introduced a protocol (CP protocol for short) for proving the equality of two discrete logarithms (EQDL) with unconditional soundness, which is widely used nowadays and plays a central role in DL-based cryptography. Somewhat surprisingly, the CP protocol has never been improved for nearly two decades since its advent. We note that the CP protocol is usually used as a non-interactive proof by using the Fiat-Shamir heuristic, which inevitably relies on the random oracle model (ROM) and assumes that the adversary is computationally bounded. In this paper, we present an EQDL protocol in the ROM which saves approximately 40% of the computational cost and approximately 33% of the prover??s outgoing message size when instantiated with the same security parameter. The catch is that our security guarantee only holds for computationally bounded adversaries. Our idea can be naturally extended for simultaneously showing the equality of n discrete logarithms with O(1)-size commitment, in contrast to the n-element adaption of the CP protocol which requires O(n)-size. This improvement benefits a variety of interesting cryptosystems, ranging from signatures and anonymous credential systems, to verifiable secret sharing and threshold cryptosystems. As an example, we present a signature scheme that only takes one (offline) exponentiation to sign, without utilizing pairing, relying on the standard decisional Diffie-Hellman assumption.