深度包检测技术中模式匹配算法分析

随着计算机网络技术的快速发展,P2 P应用作为发展最为迅速的应用之一,给人们带来方便的同时也影响了网络带宽资源及网络安全。深度包检测技术是P2 P应用识别中广泛使用的技术,而模式匹配算法是深度包检测的关键技术,为了提高深度包检测技术,需要高效、可靠的模式匹配算法。对一些常用的模式匹配算法进行了分析和总结,并提出未来的研究方向。     

Predicting the Q of junior researchers using data from the first years of publication

A researcher's Q denotes their ability in scientific research as a real number. Due to their short presence in the academic environment, junior researchers have unstable Q values. This article aims to present a model that uses data from junior researchers’ first years of publication to predict their stable Q values. We tested the deep model and the linear regression model and compared their accuracies. We have obtained reliable results showing that the predicted values estimated with both models are better than the estimated Q values computed with the Q model itself when using only data from the first five years of publication. Lastly, we note that both approaches are robust approaches to deal with the inflation of citation bias.     

Improving neural topic modeling via Sinkhorn divergence

Textual data have been a major form to convey internet users’ content. How to effectively and efficiently discover latent topics among them has essential theoretical and practical value. Recently, neural topic models(NTMs), especially Variational Auto-encoder-based NTMs, proved to be a successful approach for mining meaningful and interpretable topics. However, they usually suffer from two major issues:(1)Posterior collapse: KL divergence will rapidly reach zeros resulting in low-quality representation in latent distribution; (2)Unconstrained topic generative models: Topic generative models are always unconstrained, which potentially leads to discovering redundant topics. To address these issues, we propose Autoencoding Sinkhorn Topic Model based on Sinkhorn Auto-encoder(SAE) and Sinkhorn divergence. SAE utilizes Sinkhorn divergence rather than problematic KL divergence to optimize the difference between posterior and prior, which is free of posterior collapse. Then, to reduce topic redundancy, Sinkhorn Topic Diversity Regularization(STDR) is presented. STDR leverages the proposed Salient Topic Layer and Sinkhorn divergence for measuring distance between salient topic features and serves as a penalty term in loss function facilitating discovering diversified topics in training. Several experiments have been conducted on 2 popular datasets to verify our contribution. Experiment results demonstrate the effectiveness of the proposed model.     

TCACNet: Temporal and channel attention convolutional network for motor imagery classification of EEG-based BCI

Brain–computer interface (BCI) is a promising intelligent healthcare technology to improve human living quality across the lifespan, which enables assistance of movement and communication, rehabilitation of exercise and nerves, monitoring sleep quality, fatigue and emotion. Most BCI systems are based on motor imagery electroencephalogram (MI-EEG) due to its advantages of sensory organs affection, operation at free will and etc. However, MI-EEG classification, a core problem in BCI systems, suffers from two critical challenges: the EEG signal’s temporal non-stationarity and the nonuniform information distribution over different electrode channels. To address these two challenges, this paper proposes TCACNet, a temporal and channel attention convolutional network for MI-EEG classification. TCACNet leverages a novel attention mechanism module and a well-designed network architecture to process the EEG signals. The former enables the TCACNet to pay more attention to signals of task-related time slices and electrode channels, supporting the latter to make accurate classification decisions. We compare the proposed TCACNet with other state-of-the-art deep learning baselines on two open source EEG datasets. Experimental results show that TCACNet achieves 11.4% and 7.9% classification accuracy improvement on two datasets respectively. Additionally, TCACNet achieves the same accuracy as other baselines with about 50% less training data. In terms of classification accuracy and data efficiency, the superiority of the TCACNet over advanced baselines demonstrates its practical value for BCI systems.     

我国e-Learning环境深度学习研究述评

建设学习型社会、构筑终身学习体系是国际教育大趋势,培养学习者在真实情景和多重技术环境下深度学习能力已成为重要的时代命题。本文对深度学习的产生和发展进行历史回朔,利用内容分析法从研究方向、研究范畴、研究类型及应用对象对国内10年来e-Learning环境下深度学习的相关刊文献进行梳理,同时对文献被引频次和关键词词频进行排序分析。研究得出4点结论：一是研究日趋多元与深入,呈现全面发展格局;二是实证研究得到重视,设计及行动研究有待深入;三是学习理论、技术观和教学理论与其存在密切关联;四是大视角、游戏与开放环境的研究成为未来关注重点。     

对大型深水库水温分层和滞温效应原因的分析

大型深水库会形成水温分层现象,部分深水库还会产生滞温效应,对下游生产及生态环境造成一定的不利影响。文章从物理原理上探讨大型深水库水温分层和滞温效应的原因,对当前水库水温的计算公式理论提供依据。     

基于情绪分析的智慧图书馆阅读推广服务研究--以老年群体抑郁情绪分析为例

阅读推广服务是智慧图书馆建设的重要内容,通过智慧图书馆阅读推广服务向老年人提供高质量阅读服务是公共图书馆的重要职责。基于情绪分析的智慧图书馆阅读服务平台获取老年人社交媒体数据,可用深度学习的长短时记忆模型分析其潜在抑郁倾向。本文从5个维度设计了智慧图书馆服务质量的量化评价指标,并探讨了实施老年人智慧图书馆阅读推广服务应采取的对策建议,提出了智慧图书馆面向特定目标人群的阅读推广服务模式,设计了读者视角智慧阅读推广服务的自动量化评价方法,数据客观,消除了以往评价中的主观因素。     

基于数据集成的高校图书馆个性化信息服务研究

本文论述了高校图书馆开展个性化信息服务的现状,Deep web数据集成的加入可以使得个性化信息服务更有优势,分析了Deep web的整合方法和数据集成系统框架,并在此基础上设计了高校图书馆个性化信息服务的系统.     

构建深度学习三大“真学支架”

根据我们目前的教育情况来说,深度学习的三大“真学支架”非常有必要。在平时的课堂教育中我们应该要求学生在学习的过程中采用深度学习方法,同时教师也应该根据基本的学习原则和学生的不同心理特点对学习教材进行深度的挖掘,以及对学生的学习方法进行耐心的引导,从而激发学生对学习的兴趣,提高学生自身的学习素养。     

科学论文功能单元本体设计与标引应用实验

科学论文内容本体是科学论文内容结构和语义功能的形式化和规范化知识表示,对于科学论文的深度标引和知识挖掘具有重要意义。本文系统梳理了已有科学论文内容表示模型和内容本体,并以功能单元理论为基础,提出科学论文功能单元本体的设计思路,构建包含28个类和5种属性在内的科学论文功能单元本体FUO。借助本体构建工具Protégé,对科学论文功能单元本体FUO进行形式化表示。借助语义标注工具GATE,利用功能单元本体FUO对论文进行初步的深度标引实验,检验了该本体的可用性。结果表明,功能单元本体FUO能够很好地表示科学论文内容组件的语义功能及其属性,揭示科学论文正文各部分的语义特征,可以用于面向知识发现的科学论文深度语义标引,为科学论文内容本体开发奠定了基础。图4。表9。参考文献47。