Idea plagiarism detection with recurrent neural networks and vector space model

Purpose-Natural languages have a fundamental quality of suppleness that makes it possible to present a single idea in plenty of different ways.This feature is often exploited in the academic world,leading to the theft of work referred to as plagiarism.Many approaches have been put forward to detect such cases based on various text features and grammatical structures of languages.However,there is a huge scope of improvement for detecting intelligent plagiarism.Design/methodology/approach-To realize this,the paper introduces a hybrid model to detect intelligent plagiarism by breaking the entire process into three stages:(1)clustering,(2)vector formulation in each cluster based on semantic roles,normalization and similarity index calculation and(3)Summary generation using encoder-decoder.An effective weighing scheme has been introduced to select terms used to build vectors based on K-means,which is calculated on the synonym set for the said term.If the value calculated in the last stage lies above a predefined threshold,only then the next semantic argument is analyzed.When the similarity score for two documents is beyond the threshold,a short summary for plagiarized documents is created.Findings-Experimental results show that this method is able to detect connotation and concealment used in idea plagiarism besides detecting literal plagiarism.Originality/value-The proposed model can help academics stay updated by providing summaries of relevant articles.It would eliminate the practice of plagiarism infesting the academic community at an unprecedented pace.The model will also accelerate the process of reviewing academic documents,aiding in the speedy publishing of research articles.

Word Embeddings and Semantic Spaces in Natural Language Processing

One of the critical hurdles, and breakthroughs, in the field of Natural Language Processing (NLP) in the last two decades has been the development of techniques for text representation that solves the so-called curse of dimensionality, a problem which plagues NLP in general given that the feature set for learning starts as a function of the size of the language in question, upwards of hundreds of thousands of terms typically. As such, much of the research and development in NLP in the last two decades has been in finding and optimizing solutions to this problem, to feature selection in NLP effectively. This paper looks at the development of these various techniques, leveraging a variety of statistical methods which rest on linguistic theories that were advanced in the middle of the last century, namely the distributional hypothesis which suggests that words that are found in similar contexts generally have similar meanings. In this survey paper we look at the development of some of the most popular of these techniques from a mathematical as well as data structure perspective, from Latent Semantic Analysis to Vector Space Models to their more modern variants which are typically referred to as word embeddings. In this review of algoriths such as Word2Vec, GloVe, ELMo and BERT, we explore the idea of semantic spaces more generally beyond applicability to NLP.

Evaluation of Modified Vector Space Representation Using ADFA-LD and ADFA-WD Datasets

Predicting anomalous behaviour of a running process using system call trace is a common practice among security community and it is still an active research area. It is a typical pattern recognition problem and can be dealt with machine learning algorithms. Standard system call datasets were employed to train these algorithms. However, advancements in operating systems made these datasets outdated and un-relevant. Australian Defence Force Academy Linux Dataset (ADFA-LD) and Australian Defence Force Academy Windows Dataset (ADFA-WD) are new generation system calls datasets that contain labelled system call traces for modern exploits and attacks on various applications. In this paper, we evaluate performance of Modified Vector Space Representation technique on ADFA-LD and ADFA-WD datasets using various classification algorithms. Our experimental results show that our method performs well and it helps accurately distinguishing process behaviour through system calls.

Hybrid Chinese Information Retrieval Model Based on the Combination of Keyword and Concept

A hybrid model that is based on the Combination of keywords and concept was put forward. The hybrid model is built on vector space model and probabilistic reasoning network. It not only can exert the advantages of keywords retrieval and concept retrieval but also can compensate for their shortcomings. Their parameters can be adjusted according to different usage in order to accept the best information retrieval result, and it has been proved by our experiments.

基于复矢量及HSS的MMC交直流侧阻抗建模

模块化多电平换流器(modular multilevel converter,MMC)具有运行稳定、控制多样化等优点,对其精确建模成为当前研究热点。提出了一种基于复矢量的MMC交直流侧阻抗建模方法。该方法根据复矢量建模的基本原理分析了换流器的正负序转换关系,并在考虑三相耦合的前提下,从差共模角度简述了三相MMC的建模要点,建立了三相系统的完整时域模型。引入谐波状态空间理论(harmonic state space,HSS)对所建模型进行处理来提高建模精度,并利用接口矩阵及阻抗求解的基本原理建立了MMC的交直流侧阻抗模型。最后在仿真平台进行了对比实验,证实了建模方法的可行性。

基于前缀剪枝的大规模向量空间相似检索框架

针对大规模文本集合下基于权重的相似性查询问题,提出一种支持前缀剪枝的高效检索框架。首先给出向量空间模型下相似性及其带权前缀定义,理论证明了带权前缀剪枝的正确性;其次,面向大规模文本查询,提出一种新的倒排索引结构,利用索引叶节点维护记录的前缀权重,并基于该索引构建高效的相似检索算法;最后,在TF/IDF权重策略下证明该方法能够有效支持大规模带权相似检索。结果表明,其查询效率较Lucene的归并验证策略提升了5倍以上。

Orbit Weighting Scheme in the Context of Vector Space Information Retrieval

This study introduces the Orbit Weighting Scheme(OWS),a novel approach aimed at enhancing the precision and efficiency of Vector Space information retrieval(IR)models,which have traditionally relied on weighting schemes like tf-idf and BM25.These conventional methods often struggle with accurately capturing document relevance,leading to inefficiencies in both retrieval performance and index size management.OWS proposes a dynamic weighting mechanism that evaluates the significance of terms based on their orbital position within the vector space,emphasizing term relationships and distribution patterns overlooked by existing models.Our research focuses on evaluating OWS’s impact on model accuracy using Information Retrieval metrics like Recall,Precision,InterpolatedAverage Precision(IAP),andMeanAverage Precision(MAP).Additionally,we assessOWS’s effectiveness in reducing the inverted index size,crucial for model efficiency.We compare OWS-based retrieval models against others using different schemes,including tf-idf variations and BM25Delta.Results reveal OWS’s superiority,achieving a 54%Recall and 81%MAP,and a notable 38%reduction in the inverted index size.This highlights OWS’s potential in optimizing retrieval processes and underscores the need for further research in this underrepresented area to fully leverage OWS’s capabilities in information retrieval methodologies.

Learning Hierarchical User Interest Models from Web Pages

We propose an algorithm for learning hierarchical user interest models according to the Web pages users have browsed. In this algorithm, the interests of a user are represented into a tree which is called a user interest tree, the content and the structure of which can change simultaneously to adapt to the changes in a user＇s interests. This expression represents a user＇s specific and general interests as a continuurn. In some sense, specific interests correspond to shortterm interests, while general interests correspond to longterm interests. So this representation more really reflects the users＇ interests. The algorithm can automatically model a us er＇s multiple interest domains, dynamically generate the in terest models and prune a user interest tree when the number of the nodes in it exceeds given value. Finally, we show the experiment results in a Chinese Web Site.

基于词覆盖率的语音数据集最小化方法

为解决高性能自动语音识别模型训练集采集成本高和训练成本高的问题,提出一种基于词覆盖率的语音训练集最小化方法,尽可能减少训练集所需的数据规模。该方法引入向量空间模型的概念,将所有语料文本映射到高维空间,通过计算向量之间的余弦距离来筛选相似度最低的文本数据。然后,根据选择的文本数据收集音频,实现使用尽可能少的音频数据达到最佳的识别效果。最后,使用汉明重叠方式计算新增词汇量以评估贡献度,从而优化余弦距离的筛选方式。实验表明,所提方法相较于随机的语音训练集筛选方法,在节省21.31%训练数据量的情况下可达到相同词覆盖率,并且训练集的词覆盖率与训练集所得模型的推理性能存在极强的正相关性,证明了在保持推理性能接近的前提下,可有效节省语音训练集的采集和训练成本,进而促进自动语音识别技术的进一步发展。

不同坐标系下六相PMSM单相开路容错MPC控制

目的目前六相永磁同步电机单相开路故障的模型预测容错控制的研究已逐步成为热点,本文将对α-β和d-q2种坐标系控制下的故障机理进行对比分析,并对比不同坐标系中下正常和故障容错运行模型的控制效果。方法基于矢量空间解耦坐标变换矩阵不变原理,对A相开路进行故障模型的理论计算分析,分别在α-β和d-q这2种不同坐标系中对其进行模型预测控制容错建模。最后在MATLAB/Simulink中对2种坐标系下的电机正常运行和故障容错运行中的工作性能采用相同电机参数进行实时仿真。结果仿真结果显示,正常运行时,2种坐标系下总谐波失真(THD)值分别为2.09%和2.77%;故障运行时,d-q坐标系下的THD值比α-β坐标系小了13.15%;容错运行时2种坐标系下的THD值分别为1.19%和1.79%。结论从仿真结果可以看出,d-q坐标系控制下的电机在故障时具有更稳定的性能,而在正常和容错运行状态下,2种坐标系下的控制效果几乎等效。

考虑换相过程的电网换相换流器小信号阻抗建模

建立准确的数学模型对于判断电力系统稳定性具有重要意义,其中阻抗模型凭借简单有效的优势成为判断系统稳定性的常用工具。为完成考虑换相过程的电网换相换流器阻抗建模,在dq坐标系下采用了基于平均化的小信号建模方法。首先,将换相重叠过程中各物理量的非线性变化过程利用小信号法线性化,建立了物理量之间的传递函数;然后,加入了交流电源侧的交流网络简化模型,同时考虑了定电压控制方式和锁相环的建模,得到电网换相换流器直流侧和交流侧的等效阻抗模型;最后,通过对比PSCAD/EMTDC仿真结果和模型计算结果,证明了阻抗模型的准确性。

基于分离源的高增益矩阵变换器特性分析

针对矩阵变换器(MC)存在的电压传输比限制以及现有升压型MC结构元件多、调制复杂的问题,提出了一种新型的分离源型矩阵变换器(SS-MC)。该结构采用电感、电容和3个二极管替代了双级MC中间的直流链路部件,从而构成分离源升压网络,避免了配置额外的直通占空比。对SS-MC的拓扑结构和分离源网络升压的工作原理进行了分析,阐述了其采用的空间矢量脉宽调制(SVPWM)策略,并针对SVPWM的缺点提出了基于充电预测的模型预测控制(CPB-MPC)策略。仿真和实验结果表明:所提出的SS-MC结构使用常规的SVPWM即可达到较高的电压传输比,但无源元件会存在较大的纹波;使用CPB-MPC策略可以有效抑制纹波,提高MC的输出电能质量。

基于共模电压有效值的间接矩阵变换器调制策略对比分析

间接矩阵变换器(indirect matrix converter, IMC)是一种无中间直流环节的AC-AC变换器,具有正弦输入输出特性,且功率密度大、运行效率高,在风力发电、交流传动等领域应用前景广阔;但IMC在调制运行过程中不可避免会产生共模电压,对电机造成损坏,影响系统的可靠运行。为了提升IMC运行可靠性,抑制其输出共模电压,文章以逆变级相邻四有效电压矢量调制、逆变级相邻三有效电压矢量调制、整流级相邻三有效电流矢量调制、整流级120°两有效电流矢量加零电流矢量调制和整流级加权三有效电流矢量调制共5种经典抑制IMC共模电压峰值达42.3%的调制方法为研究对象,通过对其输出共模电压有效值进行建模,考虑电压传输比对共模电压有效值的影响,量化分析共模电压有效值的变化规律,比较不同调制方法对共模电压的抑制能力。从5种调制方法的共模电压有效值对比分析结果可知,采用逆变级相邻四有效电压矢量合成的空间矢量调制方法对IMC共模电压有效值的抑制效果最佳。仿真和实验结果验证了上述理论分析结果的正确性。

六相全桥逆变器驱动PMSM的简化模型预测电流控制

全桥逆变器电机驱动或称开绕组结构虽然兼具相间电气隔离、容错性能好及器件开关频率低等优点,但存在逆变器输出电压矢量多、冗余严重且控制约束等问题。文中以共直流母线供电的六相全桥逆变器驱动六相永磁同步电机为研究对象,提出了一种基于二级矢量优化的简化模型预测电流控制算法,在实现电机定子基波电流有效跟踪控制的同时有效抑制了定子电流的谐波及零序分量。文中基于矢量空间解耦理论对六相全桥逆变器的输出电压矢量进行了分层分析,以定子基波电流控制、谐波及零序电流抑制为准则对逆变器输出的729个电压矢量进行二级优化,得到候选的12个电压矢量,以此为基础设计了模型预测电流控制算法。实验结果表明,该简化模型预测电流控制方法具有良好的动静态性能,能在基波电流控制的同时有效抑制谐波及零序电流分量,可为多相电机驱动系统研究提供一定的理论支撑。

三相PWM整流器离散空间矢量无模型预测电流控制策略

针对PWM整流器模型预测控制对系数参数准确性高度依赖的问题,提出一种基于离散空间矢量的无模型预测电流控制(MFPCC)策略。该策略通过矢量合成,在每个控制周期应用两个基本矢量,提高了预测电流的准确性;通过峰谷采样,分别测量并存储上一控制周期两个基本矢量作用下的电流梯度;并建立电流梯度方程,从而根据应用矢量的电流梯度进一步更新剩余6个未应用矢量的电流梯度,结合当前时刻的电流采样值,实现未来时刻的电流预测,得到下一时刻最优的虚拟矢量。该方法不依赖于任何系统参数,且消除了传统MFPCC策略中电流梯度更新停滞现象,降低了输出电流谐波。最后,通过实验和仿真验证了所提方法的有效性和优越性。

基于改进型M2PC控制方法的MC-PMSM控制系统

针对传统的模型预测控制(model predictive control,MPC)方法、调制模型预测控制(modulation MPC,M2PC)存在的问题,提出改进型M2PC方法,并用于矩阵变换器(matrix converter,MC)-永磁同步电机(permanent magnet synchronous motor,PMSM)系统控制。首先,给出了MC-PMSM的拓扑结构,分析了基于传统MPC、M2PC方法的MC-PMSM控制系统及其存在的问题;接着,提出了基于改进型M2PC方法,该方法不但能够固定开关频率,还能降低计算量和改善波形质量;最后,在RTDS平台上对MC-PMSM系统采用改进型M2PC方法进行仿真,并与采用传统的MPC、M2PC方法进行了比较。结果表明,所提改进型M2PC方法的控制性能优于传统的MPC、M2PC方法。

基于i-vector全局参数联合的说话人识别

以高斯通用背景模型(Gaussian mixture model-universal background model,GMM-UBM)和i-vector模型为主的说话人识别算法在实际应用中取得了不错的成绩,但i-vector说话人识别模型中存在没有充分考虑通用背景(universal background,UB)数据与训练数据耦合性的问题导致模型性能不佳。提出了基于i-vector全局参数联合(global parameter joint of identify vector,GPJ-IV)的说话人识别方法。该方法利用背景说话人特征训练得到说话人通用背景模型(universal background model,UBM),构建基于全局联合差异空间和联合信道补偿的GPJ-IV模型。通过实验测试并与传统方法进行对比,实验结果显示,所提出的GPJ-IV模型相比i-vector模型,等错误率(equal error rate,EER)和最小检测代价函数(minimum detection cost function,MinDCF)性能分别提升了58.99%和15.9%。

基于改进K-均值的微博热点话题发现方法

微博文本数据高维度、同义、多义特征明显,传统基于向量空间模型(VSM)联合K-均值的热点话题发现方法存在准确率低,计算复杂,聚类中心难以确定等问题。提出一种相关向量机(RVM)优化VSM的微博文本向量化方法,首先利用RVM的自适应特征选择能力对VSM特征向量进行降维,然后利用主成分分析(PCA)方法确定K-均值算法的初始聚类中心,进而采用K-均值算法得到聚类结果,最后根据微博转发、评论和高影响力用户数量定义热度指数,热度指数最大的话题即为当前热点话题。采用实际微博文本数据集开展实验,结果表明所提方法相对于2种传统方法的准确率分别提升7.3%和1.1%,实时性分别提升45%和53%。

基于SVM主动学习的制革企业财务管理数据分类方法

制革企业财务管理数据具备不平衡性和非线性特征,导致难以对于数据属性进行标注,因此提出了一种基于SVM主动学习的制革企业财务管理数据分类方法。依靠向量空间模型对制革企业财务管理数据特征项进行提取并降维,将降维后的数据映射到特征空间。在特征空间利用SVM主动学习机进行数据属性标注,结合分类决策概率实现制革企业财务管理数据分类。仿真实验结果表明,所提方法的制革企业财务管理数据分类效果好,容错率较低,Kappa系数较高,制革企业财务管理数据分类效果更加理想。