基于双缓冲文件系统过滤驱动技术研究

针对单缓冲技术在过滤驱动透明加解密中存在的缺陷和不足,本文提出采用双缓冲机制,在内核态创建两个缓冲区,授权程序访问解密缓冲区数据,非授权程序访问非解密缓冲区数据,两者互不影响。首先对文件系统过滤驱动和双缓冲技术进行理论研究,然后提出了系统的设计思想,建立设计方案,进行实现和应用。通过测试结果表明,该系统对透明加解密系统性能和兼容性有很大提升。     

基于模拟退火—遗传算法的非线性密钥序列生成器线性复杂度研究

计算线性等价是研究非线性密钥序列生成器线性复杂度的有效方法。本文先介绍了计算线性等价的模拟退火法,然后使用遗传算法对该算法进行改进,最后使用一组密钥序列生成器对改进后的算法进行性能评估,并将改进后的算法和原算法进行了比较。结果表明改进后的算法能比原算法更有效的找到非线性密钥序列生成器的线性等价。     

Measuring the spreadability of users in microblogs

Message forwarding (e.g.,retweeting on Twitter.com) is one of the most popular functions in many existing microblogs,and a large number of users participate in the propagation of information,for any given messages.While this large number can generate notable diversity and not all users have the same ability to diffuse the messages,this also makes it challenging to find the true users with higher spreadability,those generally rated as interesting and authoritative to diffuse the messages.In this paper,a novel method called SpreadRank is proposed to measure the spreadability of users in microblogs,considering both the time interval of retweets and the location of users in information cascades.Experiments were conducted on a real dataset from Twitter containing about 0.26 million users and 10 million tweets,and the results showed that our method is consistently better than the PageRank method with the network of retweets and the method of retweetNum which measures the spreadability according to the number of retweets.Moreover,we find that a user with more tweets or followers does not always have stronger spreadability in microblogs.     

Kinematic configuration analysis of planar mechanisms based on basic kinematic chains

According to the single-link transformation principle, and based on the topological characteristic investigations to planar closed kinematic chains (PCKCs), a general study of the kinematic configuration analysis of planar mechanisms with R-pairs is conducted in this paper. Firstly, two new concepts, contract link distance sequence and basic link group code sequence, are defined, and a novel approach for identification of multi-bar basic kinematic chains (BKCs) within the kinematic chain left from the single-link transformation is proposed. Then, another two concepts, weight code and similarity code, are defined for aid to link similarity judgment and two judgment theorems are proposed as well. As a further step, weight code or similarity code of the transformed single-link will be employed for kinematic configuration analysis situation judgment. Finally, corresponding unified constraint equations are set up and solved with coefficient homotopy method, on the basis of aforementioned analysis results. The study shows that the method is reliable and effective, and it is especially applicable for the kinematic configuration analysis problem of PCKCs with multi-bars and multi-freedoms.     

智能化网络考试监控系统开发

近年来随着高等教育事业的快速发展,为了节约教育成本、提高教学质量和教育的信息化程度,网络化、无纸化考试在高校得到了快速推广。但由于计算机网络是一个开放的系统,考试中电子文档资料可以十分隐秘地被复制和在学生间传递,造成很多严重的作弊行为。如何防范当前网络考试中考生的作弊行为及保障考试结果的公平性成为了急需解决的重要问题。本论文所开发的智能化网络考试监控系统克服了现有考试监控系统的不足,将各种网络考试实时监控信息机地结合在一起,实现了作弊行为检测、作弊行为发现及作弊行为报警三位一体的立体化管理。同时,所开发的网络考试监控系统独立于现有各网络考试系统,在不影响现有考试系统正常工作的情况下,实现了考试作弊行为的有效监控,使其具有良好的可推广性及可维护性。     

Objective evaluation of speech dysfluencies using wavelet packet transform with sample entropy

Dysfluency and stuttering are a break or interruption of normal speech such as repetition, prolongation, interjection of syllables, sounds, words or phrases and involuntary silent pauses or blocks in communication. Stuttering assessment through manual classification of speech dysfluencies is subjective, inconsistent, time consuming and prone to error. This paper proposes an objective evaluation of speech dysfluencies based on the wavelet packet transform with sample entropy features. Dysfluent speech signals are decomposed into six levels by using wavelet packet transform. Sample entropy (SampEn) features are extracted at every level of decomposition and they are used as features to characterize the speech dysfluencies (stuttered events). Three different classifiers such as k-nearest neighbor (kNN), linear discriminant analysis (LDA) based classifier and support vector machine (SVM) are used to investigate the performance of the sample entropy features for the classification of speech dysfluencies. 10-fold cross validation method is used for testing the reliability of the classifier results. The effect of different wavelet families on the classification performance is also performed. Experimental results demonstrate that the proposed features and classification algorithms give very promising classification accuracy of 96.67% with the standard deviation of 0.37 and also that the proposed method can be used to help speech language pathologist in classifying speech dysfluencies.     

Fast response fringe‐field switching mode liquid crystal development for shutter glass 3D

A type of fast response time (RT) liquid crystal (LC) was developed to adopt in fringe‐field switching (FFS) mode for shutter glass 3D display device, the average gray to gray level (GTG) RT can reduce from 7.43 ms to 4.48 ms at a refreshing rate of 60 Hz, and the transmittance can increase by 15.4% compared with conventional LC based on the same cell gap. Meanwhile, the correlation among optical efficiency, correlative color temperature (CCT) of module and retardation of LC for FFS mode was studied. Transmittance is proportional to the retardation of LC; however, the CCT is inversely proportional to the retardation. The fast response LC can keep almost the original optical efficiency and CCT.     

Speech authentication by semi-fragile speech watermarking utilizing analysis by synthesis and spectral distortion optimization

This paper proposes an improved semi-fragile speech watermarking scheme by quantization of linear prediction (LP) parameters, i.e., the inverse sine (IS) parameters. The spectral distortion due to watermark embedding is controlled to meet the ‘transparency’ criterion in speech coding. A modified bit allocation algorithm combined with watermarking is developed to determine the quantization step so that the ‘transparency’ requirement is satisfied. Due to the statistical nature, the LP coefficients estimated from the watermarked speech signal are different from the watermarked LP coefficients even in the absence of attacks. This effect is the cause of increase in decoding error and minimum authentication length. To tackle this problem, an Analysis by Synthesis (AbS) scheme is developed to reduce the difference between the estimated LP coefficients and the watermarked ones. The watermark detection threshold and minimum authentication length are then derived according to the probability of error and the signal to noise ratio (SNR) requirements. Experimental results show that the proposed AbS based method can effectively reduce the difference between the watermarked IS parameter and the extracted IS parameter when there is no attacks. In addition, the modified bit allocation algorithm can automatically find the appropriate quantization step used in the odd-even modulation so that the transparency requirement is satisfied.     

Employing both gender and emotion cues to enhance speaker identification performance in emotional talking environments

Speaker recognition performance in emotional talking environments is not as high as it is in neutral talking environments. This work focuses on proposing, implementing, and evaluating a new approach to enhance the performance in emotional talking environments. The new proposed approach is based on identifying the unknown speaker using both his/her gender and emotion cues. Both Hidden Markov Models (HMMs) and Suprasegmental Hidden Markov Models (SPHMMs) have been used as classifiers in this work. This approach has been tested on our collected emotional speech database which is composed of six emotions. The results of this work show that speaker identification performance based on using both gender and emotion cues is higher than that based on using gender cues only, emotion cues only, and neither gender nor emotion cues by 7.22 %, 4.45 %, and 19.56 %, respectively. This work also shows that the optimum speaker identification performance takes place when the classifiers are completely biased towards suprasegmental models and no impact of acoustic models in the emotional talking environments. The achieved average speaker identification performance based on the new proposed approach falls within 2.35 % of that obtained in subjective evaluation by human judges.