Securing Arabic Contents Algorithm for Smart Detecting of Illegal Tampering Attacks

The most common digital media exchanged via the Internet is in text form. The Arabic language is considered one of the most sensitive languages of content modification due to the presence of diacritics that can cause a change in the meaning. In this paper, an intelligent scheme is proposed for improving the reliability and security of the text exchanged via the Internet. The core mechanism of the proposed scheme depends on integrating the hidden Markov model and zero text watermarking techniques. The watermark key will be generated by utilizing the extracted features of the text analysis process using the third order and word level of the Markov model. The Embedding and detection processes of the proposed scheme will be performed logically without the effect of the original text. The proposed scheme is implemented using PHP with VS code IDE. The simulation results, using varying sizes of standard datasets, show that the proposed scheme can obtain high reliability and provide better accuracy of the common illegal tampering attacks. Comparison results with other baseline techniques show the added value of the proposed scheme.     

A Smart English Text Zero-Watermarking Approach Based on Third-Level Order and Word Mechanism of Markov Model

Text information is principally dependent on the natural languages. Therefore, improving security and reliability of text information exchanged via internet network has become the most difficult challenge that researchers encounter. Content authentication and tampering detection of digital contents have become a major concern in the area of communication and information exchange via the Internet. In this paper, an intelligent text Zero-Watermarking approach SETZWMWMM (Smart English Text Zero-Watermarking Approach Based on Mid-Level Order and Word Mechanism of Markov Model) has been proposed for the content authentication and tampering detection of English text contents. The SETZWMWMM approach embeds and detects the watermark logically without altering the original English text document. Based on Hidden Markov Model (HMM), Third level order of word mechanism is used to analyze the interrelationship between contexts of given English texts. The extracted features are used as a watermark information and integrated with digital zero-watermarking techniques. To detect eventual tampering, SETZWMWMM has been implemented and validated with attacked English text. Experiments were performed on four datasets of varying lengths under multiple random locations of insertion, reorder and deletion attacks. The experimental results show that our method is more sensitive and efficient for all kinds of tampering attacks with high level accuracy of tampering detection than compared methods.     

A Hybrid Intelligent Approach for Content Authentication and Tampering Detection of Arabic Text Transmitted via Internet

In this paper, a hybrid intelligent text zero-watermarking approach has been proposed by integrating text zero-watermarking and hidden Markov model as natural language processing techniques for the content authentication and tampering detection of Arabic text contents. The proposed approach known as Second order of Alphanumeric Mechanism of Markov model and Zero-Watermarking Approach (SAMMZWA). Second level order of alphanumeric mechanism based on hidden Markov model is integrated with text zero-watermarking techniques to improve the overall performance and tampering detection accuracy of the proposed approach. The SAMMZWA approach embeds and detects the watermark logically without altering the original text document. The extracted features are used as a watermark information and integrated with digital zero-watermarking techniques. To detect eventual tampering, SAMMZWA has been implemented and validated with attacked Arabic text. Experiments were performed on four datasets of varying lengths under multiple random locations of insertion, reorder and deletion attacks. The experimental results show that our method is more sensitive for all kinds of tampering attacks with high level accuracy of tampering detection than compared methods.     

A Novel Anonymous Authentication Scheme Based on Edge Computing in Internet of Vehicles

The vehicular cloud computing is an emerging technology that changes vehicle communication and underlying traffic management applications. However, cloud computing has disadvantages such as high delay, low privacy and high communication cost, which can not meet the needs of real-time interactive information of Internet of vehicles. Ensuring security and privacy in Internet of Vehicles is also regarded as one of its most important challenges. Therefore, in order to ensure the user information security and improve the real-time of vehicle information interaction, this paper proposes an anonymous authentication scheme based on edge computing. In this scheme, the concept of edge computing is introduced into the Internet of vehicles, which makes full use of the redundant computing power and storage capacity of idle edge equipment. The edge vehicle nodes are determined by simple algorithm of defining distance and resources, and the improved RSA encryption algorithm is used to encrypt the user information. The improved RSA algorithm encrypts the user information by reencrypting the encryption parameters . Compared with the traditional RSA algorithm, it can resist more attacks, so it is used to ensure the security of user information. It can not only protect the privacy of vehicles, but also avoid anonymous abuse. Simulation results show that the proposed scheme has lower computational complexity and communication overhead than the traditional anonymous scheme.     

Tamper Detection and Localization for Quranic Text Watermarking Scheme Based on Hybrid Technique

The text of the Quran is principally dependent on the Arabic language. Therefore, improving the security and reliability of the Quran’s text when it is exchanged via internet networks has become one of the most difficult challenges that researchers face today. Consequently, the diacritical marks in the Holy Quran which represent Arabic vowels () known as the kashida (or “extended letters”) must be protected from changes. The cover text of the Quran and its watermarked text are different due to the low values of the Peak Signal to Noise Ratio (PSNR), and Normalized Cross-Correlation (NCC); thus, the location for tamper detection accuracy is low. The gap addressed in this paper to improve the security of Arabic text in the Holy Quran by using vowels with kashida. To enhance the watermarking scheme of the text of the Quran based on hybrid techniques (XOR and queuing techniques) of the purposed scheme. The methodology propose scheme consists of four phases: The first phase is pre-processing. This is followed by the second phase where an embedding process takes place to hide the data after the vowel letters wherein if the secret bit is “1”, it inserts the kashida but does not insert the kashida if the bit is “0”. The third phase is an extraction process and the last phase is to evaluate the performance of the proposed scheme by using PSNR (for the imperceptibility), and NCC (for the security of the watermarking). Experiments were performed on three datasets of varying lengths under multiple random locations of insertion, reorder and deletion attacks. The experimental results were revealed the improvement of the NCC by 1.76%, PSNR by 9.6% compared to available current schemes.     

An Intelligent Hybrid Mutual Authentication Scheme for Industrial Internet of Thing Networks

Internet of Things (IoT) network used for industrial management is vulnerable to different security threats due to its unstructured deployment, and dynamic communication behavior. In literature various mechanisms addressed the security issue of Industrial IoT networks, but proper maintenance of the performance reliability is among the common challenges. In this paper, we proposed an intelligent mutual authentication scheme leveraging authentication aware node (AAN) and base station (BS) to identify routing attacks in Industrial IoT networks. The AAN and BS uses the communication parameter such as a route request (RREQ), node-ID, received signal strength (RSS), and round-trip time (RTT) information to identify malicious devices and routes in the deployed network. The feasibility of the proposed model is validated in the simulation environment, where OMNeT++ was used as a simulation tool. We compare the results of the proposed model with existing field-proven schemes in terms of routing attacks detection, communication cost, latency, computational cost, and throughput. The results show that our proposed scheme surpasses the previous schemes regarding these performance parameters with the attack detection rate of 97.7 %.     

基于Gabor脸和隐马尔可夫模型的人像识别算法

提出了基于Gabor小波变换和隐马尔可夫模型的人像识别算法。该算法先对人脸图像进行多分辨率的Gabor小波变换,采用主元分析法对每个结点进行降维,最后形成Gabor脸。把Gabor脸的每个特征结作为观测向量,对隐马尔可夫模型进行了训练,并把优化的模型参数用于人脸识别。实验结果表明,本文方法识别率高,复杂度较低,对部分遮挡的图像具有较大的容忍度。     

文本配图系统的设计与实现

目的 设计并开发文本配图系统,实现面向文本数据的在线自动配图。方法 基于图片和文本之间的描述关系构建“图片-标签”二分网络,然后基于“图片-标签”的二分网络,利用重启随机游走模型进行图片与标签之间的相关性计算。采用TextRank模型提取关键字,并将关键字构成的集合作为查询,将关键字视为标签。基于离线计算结果,在线整合标签与图片之间的相关性,得到文本与图片的相关性。依据相关性由大到小进行排序,并返回前k个最相关的图片。结果 实验结果表明,前5个返回结果的MAP值能够达到0.839,能够准确地返回用户期望的图片。结论 系统能够依据输入文本进行准确的图片匹配。     

无端点检测汉语识别算法的实现及改进——动态时间规整和隐马尔可夫统一模型的应用

语音识别算法中，动态时间规整（ＤＴＷ）和隐马尔可规模（ＨＭＭ）是最有效的识别算法并且两者之间有着本质的联系和内在的统一，据此前期工作中，已经建立了ＤＴＷ和ＨＭＭ的统一模型（ＤＨＵＭ）。本文对ＤＨＵＭ进行了改进，在ＤＨＵＭ中引起寂静段自环，并根据汉语语音的特点，提出了一种无端点检测的语音识别算法，在识别过程中，该算法无需确定语音信号起止点位置，而是从寂静段开始，直接按帧提取特征（帧长２０ｍｓ，帧间重     

一种面向信息系统集成的双向安全认证方法研究

随着计算机网络技术的发展和信息化的普及,系统集成的重要程度日益提升,而信息安全则是集成过程中必须考虑的重点因素之一。本文提出了一种面向信息系统集成的双向安全认证方法,并对其关键步骤与核心算法做了详细阐述,该方法在一定程度上为系统集成过程中的身份认证环节提供了安全保障。     

The State of the Art of Hidden Markov Models for Predictive Maintenance of Diesel Engines

The maintenance of diesel Engines is usually scheduled according to the maintenance procedures defined by manufacturers. However, the state of the art shows that the condition monitoring maintenance associated with adequate prediction algorithms allows performance improvement both by increasing the intervals between interventions and by helping to maintain reliability levels. There are many types of variables that can be used to measure equipment condition, as is the case of several types of pollutant emissions such as NO_x, CO2, HC, PM, and NOISE, among others. This is a typical problem that can be solved through a hidden Markov model, taking into account the specificity of this type of equipment. The paper describes two algorithms that can help to increase the quality of assessment of engine states and the efficiency of maintenance planning. Those are the Viterbi and Baum–Welch algorithms. The importance of how to calculate the performance index of the model by the use of the perplexity algorithm is also emphasized. In this paper, a new paradigm is proposed, designated as ecological predictive maintenance. Copyright © 2017 John Wiley & Sons, Ltd.     

无端点检测汉语识别算法的实现及改进——动态时间规整和隐马尔可夫统一模型的应用

语音识别算法中，动态时间规整（ＤＴＷ）和隐马尔可夫模型（ＨＭＭ）是最有效的识别算法，并且两者之间有着本质的联系和内在的统一［１］，据此前期工作中，已经建立了ＤＴＷ和ＨＭＭ的统一模型（ＤＨＵＭ）［２、３］。本文对ＤＨＵＭ进行了改进，在ＤＨＵＭ中引进寂静段自环，并根据汉语语音的特点，提出了一种无端点检测的语音识别算法。在识别过程中，该算法无需确定语音信号起止点位置，而是从寂静段开始，直接按帧提取特征（帧长２０ｍｓ，帧间重叠５０％），特征向量由１５阶倒谱系数和帧平均能量组成。实验中，用ＤＨＵＭ实现了该算法，对９９个相似汉语单字的识别测试结果表明：无端点检测的识别正识率为９４．９５％，正识率下降很少，但不作端点检测却降低了算法的复杂程度。为进一步改善识别性能，特征向量采用一种听觉模型特征，识别器具有更好的鲁棒性，识别率会略有提高。     

基于最优Morlet小波和隐马尔可夫模型的轴承故障诊断

摘要：提出一种从信号时频域提取故障特征的新方法,先将振动信号作Morlet小波变换,再将小波系数顺序划分成多个子列,各子列协方差矩阵的特征值为所需的特征参数。为了更有效地提取信号的振动特性及周期性成分,使用了最小香农熵准则和奇异值分解技术选择Morlet小波参数,并用比较实验证明了参数优化的有效性。状态辨识使用了连续型隐马尔可夫模型,在三种故障程度下分别实现了轴承正常状态,滚动体故障,内圈和外圈故障的正确辨识,平均精度都大于93%。     

A comparative study on the performance of several speech recognition techniques applied on the highly confusing mandarin syllables

Abstract

In this paper, the performance of several speech recognition techniques applied on the highly confusing Mandarin syllables were carefully compared, including dynamic time warping (DTW), the newly proposed DTW with superimposed weighting function (DTWW), the discrete hidden Markov models (DHMM) and the continuous hidden Markov models (CHMM). The vocabulary used here consists of 409 first tone isolated Mandarin syllables. Due to the fact that many confusing sets exist in this vocabulary, the accurate recognition of these syllables is relatively difficult, and all the recognition experiments were performed in the speaker dependent mode. After a series of 13 experiments, it was found that the recognition rate of the newly proposed DTWW (88.3) is higher than that of DTW (85.1), DHMM (65.0) and CHMM (83.9), and that the CPU time used for DTWW is 1.03 times that for DTW, 24 times that for DHMM and 4.3 times that for CHMM. In addition, the memory space required for DTWW and DTW is 3.4 times that of DHMM and 8.5 times that of CHMM. Therefore, DTWW has the highest recognition rate, DHMM has the fastest recognition speed, whereas CHMM appears to be very attractive when all the different factors including recognition rate, recognition speed and memory space requirement are considered.     

Fault diagnosis method for an Aeroengine Based on Independent Component Analysis and the Discrete Hidden Markov Model

The vibration signals of an aeroengine are a very important information source for fault diagnosis and condition monitoring. Considering the nonstationarity and low repeatability of the vibration signals, it is necessary to find a corresponding method for feature extraction and fault recognition. In this paper, based on Independent Component Analysis (ICA) and the Discrete Hidden Markov Model (DHMM), a new fault diagnosis approach named ICA-DHMM is proposed. In this method, ICA separates the source signals ...     

基于自适应交互式多模型的永磁同步电机无感控制

目的 改善包装印刷机器的控制性能,减少包装印刷机器所用传感器数量和电机控制系统成本,减小包装印刷机械装置故障率和电机体积,针对传统扩展卡尔曼滤波算法中模型可能不匹配实际工况的问题,提出一种自适应多模型无感控制策略。方法 基于传统扩展卡尔曼滤波,引入多模型,在输入环节依靠状态转移概率矩阵实现多个模型间的交互,并借助隐马尔可夫模型,设计多模型的状态序列和观测序列,将观测得到的矩阵对多模型交互环节的状态转移概率矩阵进行迭代更新,提高模型面对环境扰动时的匹配程度。结果 Matlab/Simulink仿真结果表明,改进后的算法使转速的估计精度得到显著提升,同时在面对环境扰动时,其抗扰动能力显著提高。结论 与传统扩展卡尔曼滤波算法相比,改进算法提高了系统控制精度,提高了动态性能和鲁棒性,改进后算法更适合应用于包装印刷机械。     

智能手机背面屏盲打输入法键盘布局设计研究

目的 目前基于移动设备背面屏输入技术的相关研究,大多集中于其在物理键盘布局或是体型较大的移动设备(如iPad)上的探究,而基于小型移动设备(如手机)的背面屏盲打输入法键盘布局的相关研究并不多见.手机背面虚拟键盘的使用可极大程度释放前屏空间,实现前屏交互任务与背屏文本输入的同步进行.基于此研究意义,探讨智能手机背面屏盲打输入法的可行性及设计方案.方法 通过用户访谈和文本输入测试,采集并分析24167条用户输入数据.结果 智能手机背面屏的横向QWERTY键盘布局为左右拆分式,整体位于双手手持时偏上部的位置.结论 通过对用户在智能手机背面屏盲打输入偏好的研究,可以得出一个具有较强一致性的横向QWERTY键盘布局,为未来背面屏盲打输入法键盘布局设计提供指导.     

Safety analysis of safety-critical systems for their applicability on NPP systems: A state-of-the-art review

Since any risk in the safety-critical systems (SCSs) might lead to a terrible disaster, the utmost quality attribute of these systems is safety. Researchers are putting their efforts to ensure the safety of such systems. This paper summarizes the state-of-the-art in the area of the safety attribute of such critical systems. The in depth literature survey is performed to explore the several methods/models used for safety analysis of the SCS. The limitations of such methods are also shown regarding their applicability in SCS of the nuclear power plant (NPP). We also propose a framework to perform safety analysis, which can deal with these limitations and demonstrated the approach on a case study of NPP. Further, the direction for future work is advised to extend the further scope of research based on the case study.     

Hidden Markov model-based approach for generation of Pitman shorthand language symbols for consonants and vowels from spoken english

Pitman shorthand language (PSL) is a widely practised medium for transcribing/recording speech to text (StT) in English. This recording medium continues to exist in spite of considerable development in speech processing systems (SPS), because of its ability to record spoken/dictated text at high speeds of more than 120 words per minute. Hence, scope exists for exploiting this potential of PSL in present SPS. In this paper, an approach for feature extraction using Mel frequency cepstral coefficients (MFCC) and classification using hidden Markov models (HMM) for generating strokes comprising consonants and vowels (CV) in the process of production of Pitman shorthand language from spoken English is proposed. The proposed method is tested on a large number of samples, drawn from different speakers and the results are encouraging. The work is useful in total automation of PSL processing.     

基于骨导麦克风数据的咀嚼发音类型自动识别与分类方法

口腔运动与人们的饮食规律息息相关,该文通过对口腔运动状态的分析识别来监测人们的饮食规律,以此来指导人们的饮食习惯。借助语音识别技术的思想和方法,分析识别口腔运动产生的骨导音,为提升识别效率,采用了传统的隐马尔可夫模型。基于隐马尔可夫模型建立了一套骨导音识别系统,在进行骨导音识别之前,通过分帧加窗、提取梅尔频率倒谱系数,对其进行模型训练;在识别过程中,找出与待测音频信号和模板库中匹配度最高的模型,以其模型输出结果作为最后的识别结果。该方法的识别结果可以达到 84%,实验结果表明该方法具有一定的可行性。