Reviewerly/exHarmony huggingface dataset:
Event Detection and Estimation Using Distributed Population Owned Sensors

Author(s): Ibrahim, Ahmed Mokhtar Nagy | Advisor(s): Eltawil, Ahmed | Abstract: Smart phones are an indispensable tool in modern day-to-day life. Their widespread use has spawned numerous applications targeting diverse domains such as bio-medical, environment sensing and infrastructure monitoring. In such applications, the accuracy of the sensors at the core of the system is still questionable, since these devices are not originally designed for high accuracy sensing purposes. In this thesis, we investigate the accuracy limits of one of the commonly used sensors, namely, a smart phone accelerometer. As a use case, we focus on utilizing smart phone accelerometers in structural health monitoring (SHM). Using the already deployed network of distributed citizen-owned sensors is considered a cheap alternative to standalone sensors. These devices can capture floors vibration during disasters, and consequently compute the instantaneous displacement of each floor. Hence, damage indicators defined by government standards such as peak relative displacement can be estimated. In this work, we study the displacement estimation accuracy and propose a zero-velocity update (ZUPT) method for noise cancellation. Theoretical derivation and experimental validation are presented, and we discuss the impact of sensor error on the achieved building classification accuracy. Moreover, in spite of the presence of sensor error, SHM systems can be resilient by adopting machine learning. Several algorithms such as support vector machine (SVM), K-nearest neighbor (KNN) and convolutional neural network (CNN) are adopted and compared. Techniques for addressing noise levels are proposed and the results are compared to regular noise cancellation techniques such as filtering.Finally, since most previous work focused on modelling the sensor chip error itself, we study other sources of error such as sampling time uncertainty which is introduced by the device operating system (OS). That type of error can be considered a major contributor to the overall error, specially for sufficiently large signals. Hence, we propose a novel smart device accelerometer error model that includes the traditional additive noise as well as sampling time uncertainty errors. The model is validated experimentally using shake table experiments, and maximum likely-hood estimation (MLE) is used to estimate the model parameters. Moreover, we derive the Cramer-Rao lower bound (CRLB) of acceleration estimation based on the proposed model.
Comparing the Performance of the LSTM and HMM Language Models via Structural Similarity

Language models based on deep neural networks and traditional stochastic modelling have become both highly functional and effective in recent times. In this work, a general survey into the two types of language modelling is conducted. We investigate the effectiveness of the Hidden Markov Model (HMM), and the Long Short-Term Memory Model (LSTM). We analyze the hidden state structures common to both models, and present an analysis on structural similarity of the hidden states, common to both HMM's and LSTM's. We compare the LSTM's predictive accuracy and hidden state output with respect to the HMM for a varying number of hidden states. In this work, we justify that the less complex HMM can serve as an appropriate approximation of the LSTM model.
Particle Markov Chaine Monte Carlo for Power System Dynamic State Estimation

This paper introduces a novel technique for power system dynamic state estimation using particle Markov chain Monte Carlo. State estimation is a crucial important application of the energy management system. Using the phasor measurement units (PMU), a real-time tracking of the dynamic states of the power system has become possible. The particle filters (PF) became a tremendously popular tool to perform tracking in nonlinear/non-Gaussian dynamical systems. However the computational cost of the PF becomes prohibitive when applied to high dimensional state-spaces. Furthermore, particle Markov chain Monte Carlo (PMCMC), has been emerged recently as an effective approach in tracking nonlinear dynamics high-dimensional systems. Therefore, this paper presents a comparison between the standard particle filter and the particle Markov chain Monte Carlo, in power system dynamic state estimation. The simulation results of the IEEE New England 39-bus test system exhibits an effective performance of the PMCMC compared to PF, and demonstrate that the PMCMC provides an alternative solution for estimating the dynamic state of the electrical power systems.
Few-weight codes over $\Bbb F_p+u\Bbb F_p$ associated with down sets and their distance optimal Gray image

Let $p$ be an odd prime number. In this paper, we construct $2(2p-3)$ classes of codes over the ring $R=\Bbb F_p+u\Bbb F_p,u^2=0$, which are associated with down sets. We compute the Lee weight distributions of the $2(2p-3)$ classes of codes when the down sets are generated by a single maximal element. Moreover, by using the Gray map of the linear codes over $R$, we find out $2(p-1)$ classes of $p$-ary distance optimal linear codes. Two of them meet the Griesmer bound.
Bounded-Length Smith-Waterman Alignment

Given a fixed alignment scoring scheme, the bounded length (respectively, bounded total length) Smith-Waterman alignment problem on a pair of strings of lengths m, n, asks for the maximum alignment score across all substring pairs, such that the first substring's length (respectively, the sum of the two substrings' lengths) is above the given threshold w. The latter problem was introduced by Arslan and Egecioglu under the name local alignment with length threshold. They proposed a dynamic programming algorithm solving the problem in time O(mn^2), and also an approximation algorithm running in time O(rmn), where r is a parameter controlling the accuracy of approximation. We show that both these problems can be solved exactly in time O(mn), assuming a rational scoring scheme; furthermore, this solution can be used to obtain an exact algorithm for the normalised bounded total length Smith - Waterman alignment problem, running in time O(mn log n). Our algorithms rely on the techniques of fast window-substring alignment and implicit unit-Monge matrix searching, developed previously by the author and others.
A game method for improving the interpretability of convolution neural network

Real artificial intelligence always has been focused on by many machine learning researchers, especially in the area of deep learning. However deep neural network is hard to be understood and explained, and sometimes, even metaphysics. The reason is, we believe that: the network is essentially a perceptual model. Therefore, we believe that in order to complete complex intelligent activities from simple perception, it is necessary to con-struct another interpretable logical network to form accurate and reasonable responses and explanations to external things. Researchers like Bolei Zhou and Quanshi Zhang have found many explanatory rules for deep feature extraction aimed at the feature extraction stage of convolution neural network. However, although researchers like Marco Gori have also made great efforts to improve the interpretability of the fully connected layers of the network, the problem is also very difficult. This paper firstly analyzes its reason. Then a method of constructing logical network based on the fully connected layers and extracting logical relation between input and output of the layers is proposed. The game process between perceptual learning and logical abstract cognitive learning is implemented to improve the interpretable performance of deep learning process and deep learning model. The benefits of our approach are illustrated on benchmark data sets and in real-world experiments.
Identifying the Most Explainable Classifier

We introduce the notion of pointwise coverage to measure the explainability properties of machine learning classifiers. An explanation for a prediction is a definably simple region of the feature space sharing the same label as the prediction, and the coverage of an explanation measures its size or generalizability. With this notion of explanation, we investigate whether or not there is a natural characterization of the most explainable classifier. According with our intuitions, we prove that the binary linear classifier is uniquely the most explainable classifier up to negligible sets.
Binary Image Steganography using Flipping Distortion Technique

In binary image steganography many methods are used. The flipping distortion of the pixel is important to hide the message in image. The binary steganography image is divided into 3X3 blocks. Binary image encrypted with a private key .In this method non-overlapped blocks is take and scan image from top left corner. To decrypt the message string user need private key. Some techniques like minimizing flipping distortion are used securely data transfer. In steganographic scheme divide the scrambled image into superpixels and generate cover image. In this method, the image divided into blocks of order 3 X 3. Each block is divided diagonally and the number of black and white pixel in both the upper and lower halves of the block is determined. After that connectivity and smoothness test is performed on each 3X3 block to decide whether block is embeddable or not. For extraction the hidden data bits, reverse process is applied on the image. Hence original image is not use for extraction.
Syntactic Complexity of Russian Unified State Exam Texts in English: A Study on Reliability and Validity

In this study we analyze texts used in Russian Unified State Exam on English language. Texts that formed small research corpora were retrieved from 2 resources: official USE database as a reference point, and popular website used by pupils for USE training “Neznaika” (https://neznaika.pro/). The size of two corpora is balanced: USE has 11934 tokens and “Neznaika” - 11918 tokens. We share Biber’s point of view that linguistic tendencies are quite stable with ten text samples per genre or register (Biber 2007). We retrieved 20 texts from each resource. This research takes into consideration analysis on syntactic complexity, the main subject of research is the syntactic type of the sentence. The present study focuses on two research questions: RQ1: What sentence types pattern is typical for USE texts? RQ2: Are the materials of the training sites reliable and valid? The methods employed in the study are the identification and manual counting of the sentence types, absolute and normalized frequency calculation. While analyzing the texts, we witnessed greater range of tokens per text (tpt) in unofficial texts for training. For “Neznaika” database the range was 490 - 790 (tpt), while an official USE database texts demonstrated lower variance: 539 – 686 tpt. The number of sentences in “Neznaika” (664) and official USE texts database (670) is almost equal. The number of sentence types in “Neznaika” and official USE texts database also does not extend correlation limits.
GSU Library Research Guides: PACE Consulting Resources: Research Resources

Recommended resources for students to use on PACE consulting projects How to find research articles, news articles, data and other information
A Large Multilingual Corpus of Pashto, Urdu, English for Automatic Spoken Language Identification

The availability of a standard and phonetically rich speech corpus provides a common platform for comparing the performance of different speech recognition approaches and therefore is the first step for the research in a language.This work presents the development of a large multilingual speech corpus of Pashto, Urdu and English.Recordings have been made from a total of 194 speakers in the three languages, covering diverse dialects, age groups, genders and professions.Pashto and Urdu both native and non-native speakers have been considered while for English, all the speakers were non-native.The corpus comprises of three categories of phonetically rich spoken data in each language, that is, short questions regarding speaker's personal information, read speech and spontaneous speech from the domain of tourism.Although the corpus is developed primarily for research on Automatic Spoken Language Identification purpose, nevertheless, it can also be used for research on other topics such as Automatic Speech Recognition, Accent Recognition, Automatic Speaker Identification and the study of effects of non-nativeness on Language and Speaker Identification.
All-Action Policy Gradient Methods: A Numerical Integration Approach

While often stated as an instance of the likelihood ratio trick [Rubinstein, 1989], the original policy gradient theorem [Sutton, 1999] involves an integral over the action space. When this integral can be computed, the resulting "all-action" estimator [Sutton, 2001] provides a conditioning effect [Bratley, 1987] reducing the variance significantly compared to the REINFORCE estimator [Williams, 1992]. In this paper, we adopt a numerical integration perspective to broaden the applicability of the all-action estimator to general spaces and to any function class for the policy or critic components, beyond the Gaussian case considered by [Ciosek, 2018]. In addition, we provide a new theoretical result on the effect of using a biased critic which offers more guidance than the previous "compatible features" condition of [Sutton, 1999]. We demonstrate the benefit of our approach in continuous control tasks with nonlinear function approximation. Our results show improved performance and sample efficiency.
Adversarial Regression. Generative Adversarial Networks for Non-Linear Regression: Theory and Assessment

Adversarial Regression is a proposition to perform high dimensional non-linear regression with uncertainty estimation. We used Conditional Generative Adversarial Network to obtain an estimate of the full predictive distribution for a new observation. Generative Adversarial Networks (GAN) are implicit generative models which produce samples from a distribution approximating the distribution of the data. The conditional version of it (CGAN) takes the following expression: $\min\limits_G \max\limits_D V(D, G) = \mathbb{E}_{x\sim p_{r}(x)} [log(D(x, y))] + \mathbb{E}_{z\sim p_{z}(z)} [log (1-D(G(z, y)))]$. An approximate solution can be found by training simultaneously two neural networks to model D and G and feeding G with a random noise vector $z$. After training, we have that $G(z, y)\mathrel{\dot\sim} p_{data}(x, y)$. By fixing $y$, we have $G(z|y) \mathrel{\dot\sim} p{data}(x|y)$. By sampling $z$, we can therefore obtain samples following approximately $p(x|y)$, which is the predictive distribution of $x$ for a new $y$. We ran experiments to test various loss functions, data distributions, sample size, size of the noise vector, etc. Even if we observed differences, no experiment outperformed consistently the others. The quality of CGAN for regression relies on fine-tuning a range of hyperparameters. In a broader view, the results show that CGANs are very promising methods to perform uncertainty estimation for high dimensional non-linear regression.
Trend and Prospect of History Classes on the March 1st Movement

The study is an attempt to examine the trends of the March 1st Movement classes over the past 30 years. This paper distinguished three periods based on changes in teachers" interests and views on the March 1st Movement. The first period is from the 1990s to 2008. Practices for teaching the March 1st Movement began to be announced. Classes during this period have since become the basis for the subsequent classes. The second period is from 2009 to 2015. There were new attempts to improve the perspective, content, and methods of the March 1st Movement in existing classes. The final period is from 2016 to the present. The March 1st Movement classes were practiced a lot more and the contents and methods of the class were improved. There were five main types in the March 1st Movement classes. The first is an explanatory class that examines the background, development process and significance of the March 1st Movement. The second is to recreate the situation where the March 1st Movement took place. The third is to read and write the Declaration of Korean Independence. The fourth is a class that deals with the March 1st Movement that took place in local areas. The last is to discuss issues and ideas concerning the March 1st Movement. Over the past 30 years, the March 1st Movement classes have changed in many ways, including perspective, content, and method. The March 1st Movement classes were influenced by changes in the memories of the March 1st Movement in our society.
Flexible calibration method for fringe projection profilometry system using spheres

A newly developed flexible calibration algorithm for fringe projection profilometry system is presented in this paper. Previous studies have exploited images of spheres to calibrate the camera. It is shown in this paper that this approach can be improved to suit for the projector and ultimately achieve the overall calibration of FPP (Fringe Projection Profilometry) system. Taking the projector as a virtual camera, the images of sphere contour on the projectors plane can also be obtained through the phase information. The derivation and acquisition of intrinsic parameters for projector are just the same way used in the camera. In our algorithm, at least 3 images of sphere contour on both camera and projector are obtained to calculate the homography between these two views. Then the image of the sphere and its shadow on an induced plane settled in the back of the sphere are added to recover the epipolar geometry for the FPP system. Experimental results on real data are presented, which demonstrate the feasibility and accuracy achieved by our proposed algorithm.
A Bibliographical Study on the Authorized Textbooks for Secondary School History During the Period of the ‘Course of Study’

This study intends to compile a list of authorized history textbooks for secondary schools during the period of the ‘Course of Study’. An analysis of the history textbooks in question led to the identification of the types and scale of publications during the period: eight “Korean History” textbooks, eight “Oriental History” textbooks, seven “Occidental History” textbooks, four “Korean Cultural History” textbooks, three
A Note on a Unifying Proof of the Undecidability of Several Diagrammatic Properties of Term Rewriting Systems

In this note we give a simple unifying proof of the undecidability of several diagrammatic properties of term rewriting systems that include: local confluence, strong confluence, diamond property, subcommutative property, and the existence of successor. The idea is to code configurations of Turing Machines into terms, and then define a suitable relation on those terms such that the termination of the Turing Machine becomes equivalent to the satisfiability of the diagrammatic property.
Monte Carlo Co-Ordinate Ascent Variational Inference

In Variational Inference (VI), coordinate-ascent and gradient-based approaches are two major types of algorithms for approximating difficult-to-compute probability densities. In real-world implementations of complex models, Monte Carlo methods are widely used to estimate expectations in coordinate-ascent approaches and gradients in derivative-driven ones. We discuss a Monte Carlo Co-ordinate Ascent VI (MC-CAVI) algorithm that makes use of Markov chain Monte Carlo (MCMC) methods in the calculation of expectations required within Co-ordinate Ascent VI (CAVI). We show that, under regularity conditions, an MC-CAVI recursion will get arbitrarily close to a maximiser of the evidence lower bound (ELBO) with any given high probability. In numerical examples, the performance of MC-CAVI algorithm is compared with that of MCMC and -- as a representative of derivative-based VI methods -- of Black Box VI (BBVI). We discuss and demonstrate MC-CAVI's suitability for models with hard constraints in simulated and real examples. We compare MC-CAVI's performance with that of MCMC in an important complex model used in Nuclear Magnetic Resonance (NMR) spectroscopy data analysis -- BBVI is nearly impossible to be employed in this setting due to the hard constraints involved in the model.
Performance of Differential CSK under Color Noise: A Comparison with CSK

In this research, we present a comprehensive study on the performance of chaotic communication systems under Gaussian and color noise.We selected the most useful systems for short range communications: Chaos Shift Key (CSK) and Differential Chaos Shift Key (DCSK) systems.Logistic map and tent map have been used to generate Chaos in these systems.Color noise has been generated by filtering Gaussian noise via.an FIR digital filter.Various spreading factors have been used.Although, DCSK outperforms CSK in complexity, it is shown that CSK outperforms DCSK under Gaussian and color noise and various spreading Factors (SF) in terms of Bit Error Rate (BER).In addition, it is shown that CSK is more secure than DCSK.both systems have been simulated using MATLAB R2018a.
Designing Gestures for Continuous Sonic Interaction

We present a system that allows users to try different ways to train neural networks and temporal modelling to asso- ciate gestures with time-varying sound. We created a soft- ware framework for this and evaluated it in a workshop- based study. We build upon research in sound tracing and mapping-by-demonstration to ask participants to de- sign gestures for performing time-varying sounds using a multimodal, inertial measurement (IMU) and muscle sens- ing (EMG) device. We presented the user with two classical techniques from the literature, Static Position regression and Hidden Markov based temporal modelling, and pro- pose a new technique for capturing gesture anchor points on the fly as training data for neural network based regression, called Windowed Regression. Our results show trade- offs between accurate, predictable reproduction of source sounds and exploration of the gesture-sound space. Several users were attracted to our windowed regression technique. This paper will be of interest to musicians engaged in going from sound design to gesture design and offers a workflow for interactive machine learning.
Data Security in Internet of Things: A Review

In recent years, Internet of Things (IoT) has become an interesting research topic in various fields such as technical, academic, medical and industry. With the growing interest in IoT, security must be taken into consideration as it is one of the main issues of IoT platform, technologies and application. The most important part of secure IoT system is verification and authentication between IoT enabled devices and IoT web servers. This paper presents reviews the research on IoT architecture, various securities issues and challenges that must be taken into consideration and also presents a multi-factor authentication for a new solution to ensure confidentiality, integrity, authentication, authorization and ability to identify heterogeneous devices.