Reviewerly/exHarmony huggingface dataset:
Reconstruction of kHz-rate 3-D flame image sequences from a low-rate 2-D recording via a data-driven approach

Diagnostics tools are the underpinnings for the experimental study of combustion phenomena. The inherent dynamic and three-dimensional (3-D) nature of turbulent flames has imposed strict requirements to the measurement techniques, which should provide both temporally and spatially resolved information of the target flames. Time-resolved volumetric tomography is one of such methods that meet the stringent demands of combustion diagnostics. However, this technique usually suffers from both high computational and experimental costs. This work aims to mitigate its limitations by developing a hybrid deep neural network that integrates the classical convolutional neural network with a state-of-the-art video interpolation model. Such a network can produce high frame rate 3-D flame voxels from low frame rate two dimensional (2-D) images, reducing the computational costs and at the same time relaxing the hardware requirement. Our study has shown that the temporal resolution can be enhanced by 15-fold. Thus, kilohertz (kHz)-rate flame tomography can potentially be realized with cost-effective industrial cameras. This also facilitates the study of ultra-rapid combustion phenomena, which cannot be resolved (greater than megahertz required) even with the most expensive commercial high-speed cameras. This technique has also been found to have a strong noise immunity, and acceptable results can still be obtained even when the noise level reaches 30%.
Effect of surface normal variability on local surface strain measurements in StereoDIC

Surface shape and surface displacement measurements for non-planar specimens are obtainable using a stereo vision system with digital image correlation (StereoDIC). The surface measurements can be employed with appropriate local surface fitting methods to extract full-field estimates for surface strains on the specimen surface. Since StereoDIC provides a dense set of 3D surface position measurements, Xi, for the entire field of view in the stereo vision system, it has been shown that these measurements will have inherent variability. This variability will propagate into any optimally defined local surface representation, including the surface's local unit vectors, such as the surface normal, n; a surface tangent, t, and the cross product, b = n x t. Once the unit vectors are estimated at surface position, Y, the surface representation at this point can be used with the orthogonal unit vectors n, b and t to obtain a set of local surface strains at Y, with variability in the initial surface shape measurements propagating into variations in the strain estimates. In the enclosed work, the authors develop a set of equations for local strain variability due to inaccuracies in the local unit vectors n, b, and t. Results are reported for the case where the unit vectors are obtained assuming a local planar fit to an N x N set of measurements Xi, and strains are determined along the two estimated in-plane directions b and t. Simulations show that the resulting strains have variability that is a strong function of the estimated normal, n. Specifically, simulations show that the estimated local strains have errors that are a strong function of both the direction of nand variability in the estimated normal, n, relative to the stereo system bisector, even if the surface strains in the original directions are exact. Experimental studies for cylindrical specimens are shown to be in agreement with the trends predicted by the simulations, demonstrating the potential importance of surface normal estimation in experimental studies.
On Validity of Sentiment Analysis Scores and Development of Classification Model for Student-Lecturer Comments Using Weight-based Approach and Deep Learning

In this paper, a novel state-of-art classification method was presented for student-lecturer comment classification. Tf-Idf was used to assign weights for each word and several different ANN structures were tested. A large dataset, 52571 comments, was used during training. The results show that developed models clearly overperformed existing classification models in this field. 97% of prediction accuracy was achieved on 3-class dataset, while the prediction accuracy for 5-class dataset was 92%.
Smart algorithmic based web crawling and scraping with template autoupdate capabilities

Summary Web scraping is the process of extracting data from web pages and it is an essential part for the generation of datasets. Currently the field is dominated by capable commercial applications, however, there is always a need for web crawling and web scraping applications for custom projects. Developing fit for purpose tools for retrieving and structuring data from web services, cloud systems, and big data is a challenging task. Based on empirical studies, some of the challenges include structural issues, formatting/ presentation, availability, denial of service, size, and information fetching problems with browsers. Additionally, the data become inaccessible after the structure/template of the website changes for example, after the website update. Thus the dataset cannot be updated in the future without manually modifying the parameters of the Web Scraper. In this paper we propose an algorithm capable of autocorrecting the template (web scraping parameters) used for locating the target data and dealing with some common empirical problems. This is very useful in case there is a need for updating the dataset later, as usually, websites tend to change their pages. Moreover, we introduce an implementation of the algorithm via a tool developed for extracting data from the unity asset store. The tool can capture and store data in XML format. The tool extracted a total of 46 785 (40 611 3D and 6174 2D) items, with 35 successful first retries, 11 second retries and 5 fails.
Convolutional Neural Network based Deep Feature Extraction in Remote Sensing Images

Remote sensing is an important process that helps in the research and development of any country across the globe. Likewise, object recognition and object classification techniques provide major contributions in the development of artificial intelligence and machine learning algorithms. The main aim of these methods is to teach the computers to acquire, process and predict the data for future developments. Object identification and classification methods are used everywhere these days. For example, industry, medical imaging, space technologies, forensic etc. One the field that highly utilizing the remote sensing and object recognition method is the defense system. This paper proposes a method to identify the aircraft from the remote sensing images by using deep learning and image processing techniques. The process consists of two stages training stages and test stages. The proposed system could identify and classify the aircraft from the remote sensing images by using the convolutional neural network and deep learning technique.
Private Deep Neural Network Models Publishing for Machine Learning as a Service

Machine learning as a service has emerged recently to relieve tensions between heavy deep learning tasks and increasing application demands. A deep learning service provider could help its clients to benefit from deep learning techniques at an affordable price instead of huge resource consumption. However, the service provider may have serious concerns about model privacy when a deep neural network model is published. Previous model publishing solutions mainly depend on additional artificial noise. By adding elaborated noises to parameters or gradients during the training phase, strong privacy guarantees like differential privacy could be achieved. However, this kind of approach cannot give guarantees on some other aspects, such as the quality of the disturbingly trained model and the convergence of the modified learning algorithm. In this paper, we propose an alternative private deep neural network model publishing solution, which caused no interference in the original training phase. We provide privacy, convergence and quality guarantees for the published model at the same time. Furthermore, our solution can achieve a smaller privacy budget when compared with artificial noise based training solutions proposed in previous works. Specifically, our solution gives an acceptable test accuracy with privacy budget ϵ = 1. Meanwhile, membership inference attack accuracy will be deceased from nearly 90% to around 60% across all classes.
High-quality Activity-Level Video Advertising

Online video advertising is a billion dollar business, but the current low CTR reveals the huge potential for improvement in the ad serving quality. In this work, we present a novel activity-level video advertising system named ActVa. Different from existing systems that assume a fixed scope of ad keywords, ActVA enables advertising targeted to non-predefined activities in a highly efficient way requiring no training data for diverse activities. To achieve this goal, a general and extensible graphical representation of both video content and advertising demand is proposed to embed multimodal content at the activity level. Our ad-content relevancy measurement can achieve 10,000 FPS retrieval speed. We model the ads assigning task as an optimization problem taking content relevance, ads revenue as well as viewer experience into consideration. A non-maximal suppression based algorithm is designed to significantly reduce the algorithm complexity for online ad serving. Our extensive objective and subjective experimental results show the effectiveness and efficiency of ActVA. ActVA can effectively uncovers numerous high-quality (content-relevant) advertising opportunities and delivers ads to viewers in a profitable and user-friendly way.
Application to Optimal Control of Brushless DC Motor with ADRC Based on Genetic Algorithm

A novel active disturbance rejection control method based on genetic algorithm was proposed for brushless DC motor. Firstly, the mathematical model of the control method was analyzed, and a criterion of integral square error - integrated time and absolute error was proposed to construct the fitness function of genetic algorithm to improve the dynamic and static performance of brushless DC motor. Finally, the proposed method was compared with conventional PID control by experiment, the results show that with active disturbance rejection control optimized by genetic algorithm, the motor got faster response speed, smaller overshoot, and shock was also restrained effectively.
Eye Movement Classification Using CNN

In today's world eye has significant importance in technological advancements. Eye contact and eye gaze are an important factor for communication. Eye movement has a wide range of applications in various technology. In recent years, eye movement detection is gaining a lot of attention. Our aim is to create an approach that employs deep convolutional neural networks which will run for both eyes in parallel for visual feature extractions. Dataset images for training and validation were obtained from the eye chimera dataset. Testing is done by inputting real-time videos.
Indonesian Twitter Sentiment Analysis Using Word2Vec

Sentiment is an opinion of someone on certain topics, products, or services. Sentiment analysis is used to analyze opinions to decide whether positive or negative. Twitter is used by Indonesian to express their opinion in the form of tweets. This study used word2vec method to extract features by converting data into vector. Word2Vec has the advantage of being able to see semantic relationship between words. This study used support vector machine (SVM) for the classification method. Sentiment classification process was done through processing training data in the form of tweets then turned into a model to be tested with the test data. First, tweets were collected by crawling them via Twitter API. Keywords and hashtags that are related to disasters in Indonesia were prepared to get relevance tweets for this study. For word2vec model, this study used Wikipedia in Bahasa Indonesia as a large amount corpus for model training. The training used Continuous Bag-of-Words (CBOW) and skip-gram as word2vec model architecture that in the last evaluation would be compared for the best classification result. Feature extraction was done by view each word in tweets to its vector score on the word2vec model that had already been trained. After that, the features passed to classification step. The implementation of word2vec and SVM generated the precision of 64,4%, recall of 58%, and an f-score of 61,1%.
Image Normalization and Weighted Classification Using an Efficient Approach for SVM Classifiers

The content of massive image changing the brightest brightness is an impasse between most tests of sorted image realizations with low-resolution representation. I have done this research through image security, which will help curb crime in the coming days, and we propose a novel receipt for their strong and effective counterpart. Image classification using low levels of the image is a difficult method, so for this, I have adopted the method of automating the semantic image classification of this research and used it with different SVM classifiers, based on the normalized weighted feature support vector machine for semantic image classification. This is a novel approach given that weighted feature or normalized biased feature is applied and it is found that the normalized method is the best. It also uses normalized weighted features to compute kernel functions and train SVM. The trained SVM is then used to classify new images. During training and generalization, we displayed a decrease of identification error rate and there have been many benefits of using SVM with better performance in normalized image-cataloging systems. The importance of this technique and its role will be highlighted in the years to come.
Sentiment Analysis of Uber &amp; Ola using Deep Learning

Sentiments are the basic emotions of the human and understanding it for various problems is just like a feature extraction, where different sentiments are taken into consideration for finding problems and understanding the use case. For understanding the sentiments there are various methods, in this, the sentiment analysis is done based on twitter which uses Uber & Ola, which are part of cab services. Understanding the needs and complaints of cab service can help in boosting and solving various problems related to users that are tweeted. Deep learning plays an important role in using tweets and extracting information. Google Word2Vec is also used to generate the vocabularies between the words and make them understand the similarity, which in turn will give better outputs for the tweets. The tweets are categorized in Positive & Negative. The two deep learning algorithms used for sentimental analysis are part of a multi-layer perceptron, which are Deep Feed Forward Neural Network and Convolution Neural Network. The algorithms are trained on training data and accuracy is generated to find if it was trained properly or not. The testing data is used to check if it predicts the correct output which will tell if the algorithm is suited for the datasets or not. The programming language used for sentimental analysis is none other than python.
Efficient Computation of Conditionals in the Dempster–Shafer Belief Theoretic Framework

The Dempster-Shafer (DS) belief theory constitutes a powerful framework for modeling and reasoning with a wide variety of uncertainties due to its greater expressiveness and flexibility. As in the Bayesian probability theory, the DS theoretic (DST) conditional plays a pivotal role in DST strategies for evidence updating and fusion. However, a major limitation in employing the DST framework in practical implementations is the absence of an efficient and feasible computational framework to overcome the prohibitive computational burden DST operations entail. The work in this article addresses the pressing need for efficient DST conditional computation via the novel computational model DS-Conditional-All. It requires significantly less time and space complexity for computing the Dempster's conditional and the Fagin-Halpern conditional, the two most widely utilized DST conditional strategies. It also provides deeper insight into the DST conditional itself, and thus acts as a valuable tool for visualizing and analyzing the conditional computation. We provide a thorough analysis and experimental validation of the utility, efficiency, and implementation of the proposed data structure and algorithms. A new computational library, which we refer to as DS-Conditional-One and DS-Conditional-All (DS-COCA), is developed and harnessed in the simulations.
A comparative study of several template matching algorithms oriented to visual navigation

With the development of machine vision technology, in the process of visual navigation with images, it is necessary to match the local geometric features or global features of the images; however, the matching of local geometric features is low in accuracy and difficult to be used in tracking. In contrast, template-based global feature matching can directly use the information of the entire image, and it has high robustness to illumination variations and occlusions, so it has attracted widespread attention. At present, the classical matching algorithms based on templates mainly include Sum of Absolute Differences (SAD), Sum of Squared Differences (SSD), Normalized Cross Correlation (NCC), and Mutual Information (MI). In order to make it more reasonable to evaluate and compare the performance of the algorithms, in this paper, we decided to compare Mean Absolute Differences (MAD), Mean Square Differences (MSD), Zero-mean Normalized Cross Correlation (ZNCC), and Normalized Mutual Information (NMI). During the experiment, the Gaussian noise, illumination variations and occlusion were applied to the current image to simulate complex navigation scenes, and then matched it with the template images. The matching values obtained by the above four matching algorithms in different scenes were collectively called as alignment metric values. The matching effects of the four algorithms were evaluated from the following aspects including the smoothness of the metric value, the number of local extremums and whether the best position was in the correct alignment position. The results showed that the accuracy of MSD was greatly affected by noise and was not suitable for scenes interfered by noise, the number of local extremums of ZNCC changed greatly under the conditions of noise, illumination changes, and occlusion, the alignment metric values became unsmooth. In comparison, the NMI showed good robustness and accuracy in different conditions.
Fragmentation of Molecules by Virtual Photons from Remote Neighbors

It is shown that a molecule can dissociate by the energy transferred from a remote neighbor. This neighbor can be an excited neutral or ionic atom or molecule. If it is an atom, then the transferred energy is, of course, electronic, and in the case of molecules, it can also be vibrational. Explicit examples are given which demonstrate that the transfer can be highly efficient at distances where there is no bonding between the transmitter and the dissociating molecule.
QR factorization-based sampling set selection for bandlimited graph signals

We consider the problem of sampling vertices or nodes of graphs where signals on graphs are bandlimited and reconstructed from the signal values on the sampled nodes. Noting that sampling a subset of nodes is equivalent to constructing the sampled matrix consisting of the corresponding rows selected from the eigenvector matrix of a variation operator (e.g., graph Laplacian), we first formulate the reconstruction error as the Frobenius matrix norm of the pseudo-inverse of the sampled matrix and manipulate the reconstruction error by using QR factorization to propose a simple criterion by which greedy sampling is conducted. We show that the proposed algorithm can achieve near-optimality for the case of high SNR with the concept of approximate supermodularity and evaluate the proposed method by comparing with a near-optimal approach developed for a sensor selection problem. We also analyze the complexity of the proposed algorithm in operation count and compare with existing greedy methods, including algorithms for subset selection of matrices since sampling of graph signals is also accomplished by selecting a subset of columns from the transpose of the eigenvector matrix. We finally demonstrate through extensive experiments that the proposed algorithm achieves a competitive performance gain for signals on various graphs in terms of complexity and signal fidelity as compared with previous novel methods.
POSTER: Attacks to Federated Learning: Responsive Web User Interface to Recover Training Data from User Gradients

Local differential privacy (LDP) is an emerging privacy standard to protect individual user data. One scenario where LDP can be applied is federated learning, where each user sends in his/her user gradients to an aggregator who uses these gradients to perform stochastic gradient descent. In a case where the aggregator is untrusted and LDP is not applied to each user gradient, the aggregator can recover sensitive user data from these gradients. In this paper, we present a new interactive web demo showcasing the power of local differential privacy by visualizing federated learning with local differential privacy. Moreover, the live demo shows how LDP can prevent untrusted aggregators from recovering sensitive training data. A measure called the exp-hamming recovery is also created to show the extent of how much data the aggregator can recover.
Modeling and Analyzing Live Streaming Performance

Today, live streaming is gaining a rapid growth in use, which refers to streaming the media content recorded and broadcast in real time. In live streaming, latency is of utmost importance since smaller latency means higher user engagement. HTTP adaptive streaming (HAS) is now the most popular live streaming technology, where the video client sends HTTP requests to server to download video segments. The bitrate adaptation (ABR) algorithm inside the client determines bitrate level for every segment. It is of great help for ABR algorithm to quantify the influence of different HAS factors on streaming performance. However, existing work mainly focuses on video on demand (VoD) streaming rather than live streaming. In this paper, we theoretically analyze live streaming performance. We first establish a queuing model to describe playout buffer evolution. Based on the model, we respectively characterize rebuffering probability, rebuffering count and streaming latency, and analyze the effects of chunk arrival rate, arrival interval fluctuation, startup threshold and video skipping on them. From analysis results, we propose insights and recommendations for bitrate adaptation in live streaming and design a simple heuristic ABR algorithm leveraging them. Extensive simulations verify the insights as well as effectiveness of the designed algorithm.