A Collusion-Resistant Fingerprinting System for Restricted Distribution of Digital Documents

Digital fingerprinting is a technique that consists of inserting the ID of an authorized user in the digital content that he requests. This technique has been mainly used to trace back pirate copies of multimedia content such as images, audio, and video. This study proposes the use of state-of-the-art digital fingerprinting techniques in the context of restricted distribution of digital documents. In particular, the system proposed by Kuribayashi for multimedia content is investigated. Extensive simulations show the robustness of the proposed system against average collusion attack. Perceptual transparency of the fingerprinted documents is also studied. Moreover, by using an efficient Fast Fourier Transform core and standard computer machines it is shown that the proposed system is suitable for real-world scenarios.     

Effective pre-processing of long term noisy audio recordings: An aid to clinical monitoring

Nowadays, great attention is devoted to minimizing the discomfort caused by connection of patients to sensors for long-term monitoring of physiological parameters. Hence, the need for contact-less monitoring systems is increasingly recognized in clinical investigation. To this aim, audio signals recorded by ambient microphones are an appealing and increasing field of research: in the biomedical field, application of contact-less audio recording of long duration may concern obstructive apnoea syndrome, preterm newborns in Intensive Care Units, daily monitoring in occupational dysphonia, speech therapy, Parkinson and Alzheimer disease, monitoring of psychiatric and autistic subjects, etc. However, a significant amount of ambient noise is inevitably included in the records.Especially in the case of recordings that take a long time, manual extraction of clinically useful information from a whole record is a time-consuming operator-dependent task, the length of a whole recording (even several hours) being prohibitive both for perceptual analysis made by listening to it and for visual inspection of signal patterns. Moreover, objective measures of signal characteristics may serve clinicians as a common ground for diagnosis. Hence, automatic methods are needed to speed up and objectify the analysis task.The present work describes a new, automatic, fast and reliable method for extracting “voiced candidates” from audio recordings of long duration for both clinical and home applications.To demonstrate its effectiveness, the method is compared to existing software tools commonly used in biomedical applications using synthetic signals.     

A method to synchronise video cameras using the audio band

This paper proposes and evaluates a novel method for synchronisation of video cameras using the audio band. The method consists in generating and transmitting an audio signal through radio frequency for receivers connected to the microphone input of the cameras and inserting the signal in the audio band. In a software environment, the phase differences among the video signals are calculated and used to interpolate the synchronous 2D projections of the trajectories. The validation of the method was based on: (1) Analysis of the phase difference changes as a function of time of two video signals. (2) Comparison between the values measured with an oscilloscope and by the proposed method. (3) Estimation of the improvement in the accuracy in the measurements of the distance between two markers mounted on a rigid body during movement applying the method. The results showed that the phase difference changes in time slowly (0.150 ms/min) and linearly, even when the same model of cameras are used. The values measured by the proposed method and by oscilloscope showed equivalence (R2=0.998), the root mean square of the difference between the measurements was 0.10 ms and the maximum difference found was 0.31 ms. Applying the new method, the accuracy of the 3D reconstruction had a statistically significant improvement. The accuracy, simplicity and wide applicability of the proposed method constitute the main contributions of this work.     

Temporal separation of whale vocalizations from background oceanic noise using a power calculation

The process of analyzing audio signals in search of cetacean vocalizations is in many cases a very arduous task, requiring many complex computations, a plethora of digital processing techniques and the scrutinization of an audio signal with a fine comb to determine where the vocalizations are located. To ease this process, a computationally efficient and noise-resistant method for determining whether an audio segment contains a potential cetacean call is developed here with the help of a robust power calculation for stationary Gaussian noise signals and a recursive method for determining the mean and variance of a given sample frame. The resulting detector is tested on audio recordings containing southern right whale sounds and its performance is compared to a contemporary energy detector and a popular deep learning method. The detector exhibits good performance at moderate-to-high signal-to-noise ratio values. The detector succeeds in being easy to implement, computationally efficient to use and robust enough to accurately detect whale vocalizations in a noisy underwater environment.     

The use of phonetic motor invariants can improve automatic phoneme discrimination

We investigate the use of phonetic motor invariants (MIs), that is, recurring kinematic patterns of the human phonetic articulators, to improve automatic phoneme discrimination. Using a multi-subject database of synchronized speech and lips/tongue trajectories, we first identify MIs commonly associated with bilabial and dental consonants, and use them to simultaneously segment speech and motor signals. We then build a simple neural network-based regression schema (called Audio-Motor Map, AMM) mapping audio features of these segments to the corresponding MIs. Extensive experimental results show that (a) a small set of features extracted from the MIs, as originally gathered from articulatory sensors, are dramatically more effective than a large, state-of-the-art set of audio features, in automatically discriminating bilabials from dentals; (b) the same features, extracted from AMM-reconstructed MIs, are as effective as or better than the audio features, when testing across speakers and coarticulating phonemes; and dramatically better as noise is added to the speech signal. These results seem to support some of the claims of the motor theory of speech perception and add experimental evidence of the actual usefulness of MIs in the more general framework of automated speech recognition.     

BOX-PCR技术在微生物多样性研究中的应用

近年来在微生物多样性研究中,利用微生物基因组中广泛分布的短重复序列设计引物,选择性地扩增重复序列之间的不同基因区域,以得到大小不等的DNA扩增片段的方法日渐增多.以BOX插入因子(细菌基因组重复序列)为基础的PCR技术,具有操作简单快捷,可重复性强,容易获得较为丰富的扩增条带等特点,最初主要应用于细菌的多样性研究.目前研究发现用BoxA1R引物对微生物中的真菌、放线菌进行选择性的扩增,也能够达到很好的遗传及多样性分析的目的.本文综述了BOX-PCR指纹图谱分析技术的特点和一般步骤;结合作者对植物内生细菌的BOX-PCR指纹图谱分析体系的优化,对BOX-PCR技术的改进进行了总结:并对该技术在微生物菌株多样性研究领域的应用现状和前景进行了阐述.     

LEDPatNet19: Automated Emotion Recognition Model based on Nonlinear LED Pattern Feature Extraction Function using EEG Signals

Electroencephalography (EEG) signals collected from human brains have generally been used to diagnose diseases. Moreover, EEG signals can be used in several areas such as emotion recognition, driving fatigue detection. This work presents a new emotion recognition model by using EEG signals. The primary aim of this model is to present a highly accurate emotion recognition framework by using both a hand-crafted feature generation and a deep classifier. The presented framework uses a multilevel fused feature generation network. This network has three primary phases, which are tunable Q-factor wavelet transform (TQWT), statistical feature generation, and nonlinear textural feature generation phases. TQWT is applied to the EEG data for decomposing signals into different sub-bands and create a multilevel feature generation network. In the nonlinear feature generation, an S-box of the LED block cipher is utilized to create a pattern, which is named as Led-Pattern. Moreover, statistical feature extraction is processed using the widely used statistical moments. The proposed LED pattern and statistical feature extraction functions are applied to 18 TQWT sub-bands and an original EEG signal. Therefore, the proposed hand-crafted learning model is named LEDPatNet19. To select the most informative features, ReliefF and iterative Chi2 (RFIChi2) feature selector is deployed. The proposed model has been developed on the two EEG emotion datasets, which are GAMEEMO and DREAMER datasets. Our proposed hand-crafted learning network achieved 94.58%, 92.86%, and 94.44% classification accuracies for arousal, dominance, and valance cases of the DREAMER dataset. Furthermore, the best classification accuracy of the proposed model for the GAMEEMO dataset is equal to 99.29%. These results clearly illustrate the success of the proposed LEDPatNet19.     

An Evidence-Based Combining Classifier for Brain Signal Analysis

Nowadays, brain signals are employed in various scientific and practical fields such as Medical Science, Cognitive Science, Neuroscience, and Brain Computer Interfaces. Hence, the need for robust signal analysis methods with adequate accuracy and generalizability is inevitable. The brain signal analysis is faced with complex challenges including small sample size, high dimensionality and noisy signals. Moreover, because of the non-stationarity of brain signals and the impacts of mental states on brain function, the brain signals are associated with an inherent uncertainty. In this paper, an evidence-based combining classifiers method is proposed for brain signal analysis. This method exploits the power of combining classifiers for solving complex problems and the ability of evidence theory to model as well as to reduce the existing uncertainty. The proposed method models the uncertainty in the labels of training samples in each feature space by assigning soft and crisp labels to them. Then, some classifiers are employed to approximate the belief function corresponding to each feature space. By combining the evidence raised from each classifier through the evidence theory, more confident decisions about testing samples can be made. The obtained results by the proposed method compared to some other evidence-based and fixed rule combining methods on artificial and real datasets exhibit the ability of the proposed method in dealing with complex and uncertain classification problems.     

The evolving technology of DNA fingerprinting and its application to fisheries and aquaculture

In 1985, Alec Jeffreys reported the development of multilocus DNA fingerprinting by Southern blot-detection of hypervariable minisatellites or variable number of tandem repeat (VNTR) loci. This technology found immediate application to various forensic and scientific problems, including fisheries and aquaculture. By 1989, however, it was recognized by many researchers that inherent problems exist in the application of multilocus fingerprinting to large sample sizes as might occur in fisheries and aquaculture genetic studies. As such, individual VNTRs were cloned for single-locus DNA fingerprinting. Although single-locus fingerprinting ameliorates many of the problems associated with multilocus DNA fingerprinting, it suffers from the problem that electrophorectic anomalies of band migration within and between gels necessitates binning of alleles, thus underestimating genetic variability in a given population. Amplification of microsatellite loci by the polymerase chain reaction, however, solved many of the problems of Southern blot-based DNA fingerprinting. Moreover, microsatellites exhibit attributes that make them particularly suitable as genetic markers for numerous applications in aquaculture and fisheries research: (1) they are abundant in the genome; (2) they display varying levels of polymorphism; (3) alleles exhibit codominant Mendelian inheritance; (4) minute amounts of tissue are required for assay (e.g., dried scales or otoliths); (5) loci are conserved in related species; (6) potential for automated assay. Recent innovations in DNA fingerprinting technology developed over the past 5 years are discussed with special emphasis on microsatellites and their application to fisheries and aquaculture, e.g., behavioural and population genetics of wild species, and selection and breeding programmes for aquaculture broodstock.     

葛根资源遗传多样性和性状关联分析

通过研究葛根资源的遗传多样性和葛根表型性状与分子标记的关联分析,为葛根的分子育种和指纹图谱构建提供理论依据。鉴定分析了127份不同来源葛根资源的10个表型性状,并用ISSR标记研究127份葛根资源的遗传多样性,对ISSR标记与表型性状进行关联分析。表型性状鉴定结果显示葛根资源的10个性状变异大、多样性较好。利用ISSR标记获得多态性条带109条,平均每个引物扩增5.73条、平均Nei's基因多样性0.2085、平均Shannon's指数0.3378,最远遗传距离为0.46。ISSR分子标记聚类分析将127份资源聚为两大类,群体结构分析和PCo A分析结果类似将127份资源分为2个亚群。GLM分析发现3个与茸毛性状关联标记,MLM分析未发现与表型性状关联的标记。本研究收集的资源遗传多样性较好,葛根分子标记聚类结果与地域关系不大,综合GLM和MLM关联分析结果,本试验未发现与表型关联位点。     

Spectral jitter modeling and estimation

This paper suggests a new method for short-time jitter estimation based on a mathematical model that describes the coupling of two periodical phenomena. Specifically, jitter is modeled as the movement of one of the two periodic phenomena with respect to the other. The proposed method measures this movement indirectly by taking into account the spectral properties of the suggested model. Experiments with synthetic jitter signals showed that the suggested method produces accurate local estimates of jitter. Further evaluation was conducted on actual normal and pathological voice signals using two databases and jitter estimations from the Multi-Dimension Voice Program (MDVP) and the Praat system. Compared with the corresponding parameters from these two systems, the proposed method outperformed both in normal vs. pathological voice discrimination accuracy by at least 4%. Furthermore, it was shown that these methods actually rely on low-pass information, while the proposed method takes into account the full spectrum. The study of the short-time statistics of the jitter measurements provided by the suggested method indicates that there is a higher correlation with voice pathology compared to that obtained by the other two systems.     

Acoustic signal perception in a noisy habitat: lessons from synchronising insects

Acoustically communicating animals often have to cope with ambient noise that has the potential to interfere with the perception of conspecific signals. Here we use the synchronous display of mating signals in males of the tropical katydid Mecopoda elongata in order to assess the influence of nocturnal rainforest noise on signal perception. Loud background noise may disturb chorus synchrony either by masking the signals of males or by interaction of noisy events with the song oscillator. Phase-locked synchrony of males was studied under various signal-to-noise ratios (SNRs) using either native noise or the audio component of noise (<9 kHz). Synchronous entrainment was lost at a SNR of -3 dB when native noise was used, whereas with the audio component still 50% of chirp periods matched the pacer period at a SNR of -7 dB. Since the chirp period of solo singing males remained almost unaffected by noise, our results suggest that masking interference limits chorus synchrony by rendering conspecific signals ambiguous. Further, entrainment with periodic artificial signals indicates that synchrony is achieved by ignoring heterospecific signals and attending to a conspecific signal period. Additionally, the encoding of conspecific chirps was studied in an auditory neuron under the same background noise regimes.     

Shall I compare thee to a GM potato?

A fundamental issue in the safety assessment of genetically modified crops is the question of whether unintentional changes have occurred in the crop plant as a consequence of the genetic modification. This question was addressed recently by using a powerful metabolite fingerprinting and metabolite profiling method to assess whether genetically modified potatoes are substantially similar to their corresponding conventional cultivars.     

Rapid characterisation of deep-sea actinomycetes for biotechnology screening programmes

A continual need in natural product discovery is dereplication, that is the ability to exclude previously tested microorganisms from screening programmes. Whole-cell fingerprinting techniques offer an ideal solution to this problem because of their rapidity and reproducibility, dependence on small samples, and automation. One such technique, Curie-point pyrolysis mass spectrometry (PyMS), has been deployed for the characterisation of a unique collection of actinomycetes recovered from Pacific Ocean sediments approximately 2000 to 6500 m below sea level. This paper addresses the question: to what extent are pyrogroups, defined on the basis of PyMS fingerprinting, related to classifications derived from more conventional microbial systematics? A collection of 44 randomly chosen deep-sea rhodococci were coded and subjected to a double-blind PyMS and numerical taxonomic (NT) analysis; the latter sorted the strains into clusters (taxospecies) using large sets of equally weighted phenotypic data. At the end of the experiment the codes were disclosed and the NT classification shown to generate 6 homogeneous clusters corresponding to different deep-sea sites. The matching of these clusters with the resulting pyrogroups was very high with an overall congruence of nearly 98%. Thus, PyMS characterisation is directly ascribable to the phenotypic variation being sought for biotechnology screens. Moreover, the exquisite discriminatory power of PyMS readily revealed infraspecific diversity in these industrially important bacteria.     

Dereplication for biotechnology screening: PyMS analysis and PCR–RFLP–SSCP (PRS) profiling of 16S rRNA genes of marine and terrestrial actinomycetes

The search for exploitable biology is a major task for biotechnology-based industries. In this context, discrimination between previously tested or recovered micro-organisms (dereplication) is imperative, in order to reduce screening costs by sorting large collections of isolates, which are then subjected to further detailed evaluation. Pyrolysis mass spectrometry (PyMS) is a whole-cell fingerprinting technique that enables the rapid and reproducible sorting of micro-organisms, uses small samples and has the advantage of being fully automated. In this study, we compare chemometric fingerprinting with a ribotyping fingerprinting method, in order to investigate the extent to which pyrogroups formed by PyMS analysis relate to genetic diversity, using polymerase chain reaction-restriction fragment length polymorphism-single-strand conformational polymorphism (PRS). A mixture of environmental strains of mycolic acid containing actinomycetes was used to mimic the selection of colonies from primary isolation plates. The congruence found between the clusters defined by the chemometric and molecular fingerprinting techniques was very high and demonstrated the effectiveness of PyMS as a rapid sorting and dereplicating procedure for putatively novel strains, criteria that are critical for biotechnological screens. Moreover, PyMS analysis revealed significant variation within pyrogroups that contained strains with the same genotypic (PRS) characteristics, thus emphasising its discriminatory capacity at the infraspecies level.     

An enhanced glucose biosensor using charge transfer techniques

An enhanced glucose biosensor based on a charge transfer technique glucose sensor (CTTGS) is described and demonstrated experimentally. In the proposed CTTGS, which is accumulation method (d-gluconate+H(+)) ion perception system, the quality of output signal with "signal integration cycles" is high. With the proposed CTTGS it is possible to amplify the sensing signals without an external amplifier by using an accumulation cycle. It can be supposed that measurements of small (d-gluconate+H(+)) ion fluctuation are difficult by ion-sensitive field effect transistor (ISFET) because the theoretical maximum sensitivity is only 59 mV/pH and the small output signals are buried in the 1/f noise component of the metal-insulator-semi-conductor field-effect transistor (MISFET). Therefore, the CTTGS has many advantages, such as high sensitivity, high accuracy, high signal-to-noise ratio (SNR), and has been successfully demonstrated using a charge transfer technique. The CTTGS exhibited excellent performance for glucose with a large span (1445 mV) and good reproducibility. Moreover, the CTTGS has good sensitivity in this range of 7.22mV/mM, a lower detection limit of about 0.01 mM/L and an upper detection limit of about 200 mM/L compared with amperometric glucose analysis which has been studied recently. Under optimum conditions, the proposed CTTGS exceeds the performance of the widely used ISFET glucose sensor. The sensitivity of the CTTGS (7.22 mV/mM) was seven times higher than that of the ISFET (1 mV/mM). Furthermore, the sensitivity obtained for human glucose levels was 29.06 mV/mM with a non-linear error of +/-0.27%; the linearity is y=0.0294x+1.8612 and R(2)=0.9999, which is acceptable for clinical application. Real sample analysis is investigated in blood glucose level by our developed CTTGS ISFET system.     

An empirical study of web browsers’ resistance to traffic analysis and website fingerprinting attacks

Anonymity protocols are employed to establish encrypted tunnels to protect the privacy of Internet users from traffic analysis attacks. However, the attackers strive to infer some traffic patterns’ characteristics (e.g. packet directions, packet sizes, inter-packet timing, etc.) in order to expose the identities of Internet users and their activities. A recent and popular traffic analysis attack is called website fingerprinting which reveals the identity of websites visited by target users. Existing work in the literature studied the website fingerprinting attack using a single web browser, namely Firefox. In this paper we propose a unified traffic analysis attack model composed of a sequence of phases that demonstrate the efficiency of website fingerprinting attack using popular web browsers under Tor (The Onion Router). In addition, we reveal the main factors that affect the accuracy of website fingerprinting attack over Tor anonymous system and using different browsers. To the best of our knowledge, no previous study uncovered such factors by deploying real-world traffic analysis attack utilizing the top five web browsers. The outcomes of the research are very relevant to Internet users (individuals/companies/governments) since they allow to assess to which extent their privacy is preserved in presence of traffic analysis attacks, in particular, website fingerprinting over different browsers. A recommendation for future research direction regarding the investigation of website fingerprinting over different scenarios is also provided.     

Characterization of microbial communities using randomly amplified polymorphic DNA (RAPD).

Similarity among a number of aquatic microbial communities was examined using randomly amplified polymorphic DNA (RAPD), a common polymerase chain reaction (PCR)-based DNA fingerprinting technique. After amplification of whole-community DNA extracts, the PCR products were resolved by agarose gel electrophoresis and the band patterns compared to determine percent similarity. Twelve different primers were used to amplify approximately 100 fragments (total) from each DNA sample; the bands were scored as present or absent and the similarity between each sample was determined using Jaccard's coefficient. From this information. dendrograms were constructed and a bootstrapping procedure was used to assess how well supported the tree topologies were. Principal component analyses were also conducted as a means of visualizing the relationships among samples. Results obtained for two different experimental systems (a pair of tidal creeks and several wells in a shallow groundwater aquifer) correlated well with the temporal and spatial variations in environmental regime at the sites confirming that arbitrarily primed PCR-based DNA fingerprinting techniques such as RAPD are useful means of discriminating among microbial communities and estimating community relatedness. Moreover, this approach has several advantages over other DNA-based procedures for whole-community analysis; it is less laborious and uses smaller quantities of DNA, making it amenable to sample-intensive monitoring, and it does not depend on culturing or the use of selective PCR primers.     

The research of constructing dynamic cognition model based on brain network

Estimating the functional interactions and connections between brain regions to corresponding process in cognitive, behavioral and psychiatric domains is a central pursuit for understanding the human connectome. Few studies have examined the effects of dynamic evolution on cognitive processing and brain activation using brain network model in scalp electroencephalography (EEG) data. Aim of this study was to investigate the brain functional connectivity and construct dynamic programing model from EEG data and to evaluate a possible correlation between topological characteristics of the brain connectivity and cognitive evolution processing. Here, functional connectivity between brain regions is defined as the statistical dependence between EEG signals in different brain areas and is typically determined by calculating the relationship between regional time series using wavelet coherence. We present an accelerated dynamic programing algorithm to construct dynamic cognitive model that we found that spatially distributed regions coherence connection difference, the topologic characteristics with which they can transfer information, producing temporary network states. Our findings suggest that brain dynamics give rise to variations in complex network properties over time after variation audio stimulation, dynamic programing model gives the dynamic evolution processing at different time and frequency. In this paper, by applying a new construct approach to understand whole brain network dynamics, firstly, brain network is constructed by wavelet coherence, secondly, different time active brain regions are selected by network topological characteristics and minimum spanning tree. Finally, dynamic evolution model is constructed to understand cognitive process by dynamic programing algorithm, this model is applied to the auditory experiment, results showed that, quantitatively, more correlation was observed after variation audio stimulation, the EEG function connection dynamic evolution model on cognitive processing is feasible with wavelet coherence EEG recording.