Design and Implementation of Algorithms for Traffic Classification

Traffic analysis is the practice of using inherent characteristics of a network flow such as timings, sizes, and orderings of the packets to derive sensitive information about it. Traffic analysis techniques are used because of the extensive adoption of encryption and content-obfuscation mechanisms, making it impossible to infer any information about the flows by analyzing their content. In this thesis, we use traffic analysis to infer sensitive information for different objectives and different applications. Specifically, we investigate various applications: p2p cryptocurrencies, flow correlation, and messaging applications. Our goal is to tailor specific traffic analysis algorithms that best capture network traffic’s intrinsic characteristics in those applications for each of these applications. Also, the objective of traffic analysis is different for each of these applications. Specifically, in Bitcoin, our goal is to evaluate Bitcoin traffic’s resilience to blocking by powerful entities such as governments and ISPs. Bitcoin and similar cryptocurrencies play an important role in electronic commerce and other trust-based distributed systems because of their significant advantage over traditional currencies, including open access to global e-commerce. Therefore, it is essential to the consumers and the industry to have reliable access to their Bitcoin assets. We also examine stepping stone attacks for flow correlation. A stepping stone is a host that an attacker uses to relay her traffic to hide her identity. We introduce two fingerprinting systems, TagIt and FINN. TagIt embeds a secret fingerprint into the flows by moving the packets to specific time intervals. However, FINN utilizes DNNs to embed the fingerprint by changing the inter-packet delays (IPDs) in the flow. In messaging applications, we analyze the WhatsApp messaging service to determine if traffic leaks any sensitive information such as members’ identity in a particular conversation to the adversaries who watch their encrypted traffic. These messaging applications’ privacy is essential because these services provide an environment to dis- cuss politically sensitive subjects, making them a target to government surveillance and censorship in totalitarian countries. We take two technical approaches to design our traffic analysis techniques. The increasing use of DNN-based classifiers inspires our first direction: we train DNN classifiers to perform some specific traffic analysis task. Our second approach is to inspect and model the shape of traffic in the target application and design a statistical classifier for the expected shape of traffic. DNN- based methods are useful when the network is complex, and the traffic’s underlying noise is not linear. Also, these models do not need a meticulous analysis to extract the features. However, deep learning techniques need a vast amount of training data to work well. Therefore, they are not beneficial when there is insufficient data avail- able to train a generalized model. On the other hand, statistical methods have the advantage that they do not have training overhead

Laser Doppler vibrometer and accelerometer for vibrational analysis of the automotive components during Simulink simulation for validation

In current research, laser Doppler vibrometer (LDV) as a new diagnostic tool is utilized for non-destructive testing of the automotive equipment. LDV technique is working based on measurement of the Doppler shift of a moving object in an interference set-up. The effects of different noises are considered and eliminated from data analysis. Here, the performance of LDV technique is compared with a reference accelerometer device. Furthermore, a simulation by Matlab Simulink is added to the analysis which confirms the results of the experimental data. Results demonstrated that the laser Doppler vibrometer can measure excellently the frequencies of different automotive components for employing in industry. Therefore, it is proposed that LDV technique can be substituted with other traditional non-destructive testing methods

Optically Thick Laser-Induced Plasmas in Spectroscopic Analysis

Studies on the plasma physics has been grown over the past few decades as a major research field. The plasma can be produced by different sources such as acr, spark, electric discharge, laser and so on. The spectral radiation of the plasma which acts as its fingerprint, contains valuable information about plasma features. Characterization of plasmas by spectroscopic measurement is a powerful tool for increasing the knowledge and applications of these kinds of radiation sources. Therefore, the spectral diagnostics methods are proposed which are based on measurement of spectral lines intensity, estimation of continuous and absorption radiation, and as well as determination of shifts and halfwiths of the spectrum [1]. The fundamental characteristic parameters of the plasma, i.e., the number densities of plasma species, electron temperature, and as well as particle transport property at each plasma space can be determined by optical emission spectroscopy and utilizing appropriate methods [2]. For accurate evaluation of plasma parameters, its thickness must be thoroughly considered. Generally, the plasmas can be separated into two categories of thin and thick groups. In thin plasmas, the re-absorption of radiation is negligible. Consequently, in spectroscopic analysis, the non-self-absorbed spectral radiation is evaluated by considering the summation of all spectral emissions along the line of sight. In optically thick plasmas, the radiation trapping happens which leads to the self-absorption phenomenon in spectroscopic analysis that is explained with details in below section

Plasma modification of poly lactic acid solutions to generate high quality electrospun PLA nanofibers

Physical properties of pre-electrospinning polymer solutions play a key role in electrospinning as they strongly determine the morphology of the obtained electrospun nanofibers. In this work, an atmospheric-pressure argon plasma directly submerged in the liquid-phase was used to modify the physical properties of poly lactic acid (PLA) spinning solutions in an effort to improve their electrospinnability. The electrical characteristics of the plasma were investigated by two methods; V-I waveforms and Q-V Lissajous plots while the optical emission characteristics of the plasma were also determined using optical emission spectroscopy (OES). To perform a complete physical characterization of the plasma-modified polymer solutions, measurements of viscosity, surface tension, and electrical conductivity were performed for various PLA concentrations, plasma exposure times, gas flow rates, and applied voltages. Moreover, a fast intensified charge-couple device (ICCD) camera was used to image the bubble dynamics during the plasma treatments. In addition, morphological changes of PLA nanofibers generated from plasma-treated PLA solutions were observed by scanning electron microscopy (SEM). The performed plasma treatments were found to induce significant changes to the main physical properties of the PLA solutions, leading to an enhancement of electrospinnability and an improvement of PLA nanofiber formation

Complex Human Action Recognition in Live Videos Using Hybrid FR-DL Method

Automated human action recognition is one of the most attractive and practical research fields in computer vision, in spite of its high computational costs. In such systems, the human action labelling is based on the appearance and patterns of the motions in the video sequences; however, the conventional methodologies and classic neural networks cannot use temporal information for action recognition prediction in the upcoming frames in a video sequence. On the other hand, the computational cost of the preprocessing stage is high. In this paper, we address challenges of the preprocessing phase, by an automated selection of representative frames among the input sequences. Furthermore, we extract the key features of the representative frame rather than the entire features. We propose a hybrid technique using background subtraction and HOG, followed by application of a deep neural network and skeletal modelling method. The combination of a CNN and the LSTM recursive network is considered for feature selection and maintaining the previous information, and finally, a Softmax-KNN classifier is used for labelling human activities. We name our model as Feature Reduction & Deep Learning based action recognition method, or FR-DL in short. To evaluate the proposed method, we use the UCF dataset for the benchmarking which is widely-used among researchers in action recognition research. The dataset includes 101 complicated activities in the wild. Experimental results show a significant improvement in terms of accuracy and speed in comparison with six state-of-the-art articles

The Role of Recast on Left Hemisphere Dominant vs. Right Hemisphere Dominant Iranian EFL Learners

In order to address the issue of brain dominancy in feedback reception, the present was conducted to investigate the effect of recast on Iranian EFL left brained vs. right brained learners' learning of English past tense. The data were collected from 98 adolescent EFL learners who were studying English in language institutes in Iran. Of the two left brained groups, one group was assigned as the experimental and the other as the control group and the same procedure was followed for the two right brained learners. While the two experimental groups were provided with recast, the two control groups received no recast during the study. Descriptive statistics and one way ANOVA through SPSS.16 were conducted with respect to the research question. The analysis of the participants' performance on the posttest demonstrated that the experimental groups outperformed the control groups, and left brained learners more than right brained learners benefited from recast. As a result, the efficacy of recast in establishing new grammatical knowledge was proved. Further, the brain dominancy of the learners did affect the degree of the utility of recasts in developing grammar knowledge. The present study has pedagogical implications for both English language learning and teaching

Applications of plasma-liquid systems : a review

Plasma-liquid systems have attracted increasing attention in recent years, owing to their high potential in material processing and nanoscience, environmental remediation, sterilization, biomedicine, and food applications. Due to the multidisciplinary character of this scientific field and due to its broad range of established and promising applications, an updated overview is required, addressing the various applications of plasma-liquid systems till now. In the present review, after a brief historical introduction on this important research field, the authors aimed to bring together a wide range of applications of plasma-liquid systems, including nanomaterial processing, water analytical chemistry, water purification, plasma sterilization, plasma medicine, food preservation and agricultural processing, power transformers for high voltage switching, and polymer solution treatment. Although the general understanding of plasma-liquid interactions and their applications has grown significantly in recent decades, it is aimed here to give an updated overview on the possible applications of plasma-liquid systems. This review can be used as a guide for researchers from different fields to gain insight in the history and state-of-the-art of plasma-liquid interactions and to obtain an overview on the acquired knowledge in this field up to now

The Efficacy of Methadone Maintenance Therapy on the Quality of Life and Marital Satisfaction among Substance Users

Introduction: the present study has been conducted with the aim of examining the quality of life (QoL) and marital satisfaction before and after three months of methadone maintenance in the patients enrolled in methadone therapy center in city of Kashan.Method: In this study, a quasi-experimental with pre and post assessments was conducted. Forty-five substance users receiving methadonetherapy were selected and investigated for three months. General assessment of patients (including demographic variables) was recorded and patients completed the QoL and martial satisfaction questionnaires.Results:Paired T-test revealed that the effect of methadone on QoL and martial satisfaction was significant (p<0.001). Scores of QoL and marital satisfaction have significant difference just in job variable among the other demographic variables. Pairwise comparison confirmed that two aspects (including physical and environmental) of QoL increased at post-test relative to pre-test.Conclusion: results of this study showed that methadone maintenance therapy (MMT) can lead to a significant improvement of QoL and martial satisfaction in substance users

Social Media and Talent Development: Influencing Factors on Use Behavior and Employees’ Work Success

Despite increasing the significant role of technology and popularity of social media among the new generation of employees, there are few studies related to the impacts and benefits of utilizing social media as a human resource development (HRD) tool in organizations. Additionally, although the concept of talent development seems to becoming an important topic among human resource scholars and professionals in recent years, there is a paucity of research in which talent development (TD) is the main focus. This dissertation sought to investigate the role of influencing factors in and the potential outcomes from implementing various social media platforms in different talent development activities. In a three-journal article format, first I conducted a systematic literature review of talent development interventions. The review helped the study to (a) identify the available TD interventions, issues and challenges, (b) understand the role of social media in designing interventions, and (c) recognize the gap in the literature regarding the application of social media tools for developing talented employees. In the second quantitative study, I used the results of the systematic literature review to fill in the gap in TD literature. Therefore, the second manuscript explored the possible effects of leveraging social media as a TD intervention, since the systematic literature review of TD demonstrated that there is no study examining such effects. The third study was a practitioner guide or view point to assist HRD professionals in positioning social media for TD purposes within the organizations. The practitioner guide integrated the results of the first and the second manuscripts. I began by describing the necessity of using social media in workplace, stating the possible TD interventions that were proved to be effective, and providing a functional and convenient framework for practitioner in which different choices of social media in talent development and their potential outcomes have been displayed