Reviewerly/exHarmony huggingface dataset:
Siamese Fully Convolutional Tracker with Motion Correction

Visual tracking algorithms use cues like appearance, structure, motion etc. for locating an object in a video. We propose an ensemble tracker with two components. First, a Siamese tracker that learns object appearance from a static image. Second, motion information obtained from consecutive frames using a flow estimation network. The motion information is used to correct the predictions obtained by the appearance based tracking component of the ensemble. Complementary nature of the two components (appearance and motion) lead to performance improvement as observed in experiments performed on VOT2018 and VOT2019 datasets.
Data Leakage Agent Detection in Cloud Computing

With the advent of the Modern Era of Technology, sprung the dependence of an individual on the availability and exibility of the Internet. Today the internet is used not only for communication but also for correspondence and as a secondary shared system for information exchange, accessible to thosewho have the permission to do so. However, with the advent of this migration to internet sharing, came the need for online storage - the cloud. This made the information readily available to a multitude of users, but at the same time vulnerable to breaches by unauthorized individuals. This system aims to provide a way to securely store shared data in the cloud while also allowing for trace ability of the same in the event of a breach.The system will encrypt the data prior to storage, allow decrypted data access only to authorized users and backtrack any guilt agent in the event of aleakage.
Design and Implementation of the Weather Radar Mobile Monitoring System Based on WeChat Enterprise

Abstract In order to solve the problems of China’s new generation Doppler weather radar observation network, such as the lack of mobile monitoring platform, the untimely malfunction alarm and the slow interaction, a mobile operation monitoring system based on WeChat Enterprise has been designed, which is mainly composed of collection, processing, dispersal and feedback of radar’s operational status information, and interactive communication between different radar facilities and support personnel. This system can realize weather radar operating status monitoring based on cell phone, hierarchical automatic warning of operating status information, automatic video push of maintenance support activities and interactive feedback and exchange of information among maintenance support personnel, etc. Based on this system, the operational monitoring and technical support capabilities of China’s new generation Doppler weather radar are greatly improved, and it plays an important role in promoting the operational efficiency of the weather radar.
Clustering A Collection of Networks With Mixtures of L<sub>1</sub>-Sparse Graphical Models

Attention has turned in recently to clustering of collections networks where each network is characterized by a sparse inverse covariance matrix. Here we consider a mixture model of sparse Gaussian graphical models and develop an exact EM algorithm that improves considerably on a previous approximation. We provide numerical examples to show its superior performance.
Speech-Driven Facial Animation with Transfer Learning

In order to move a CG model, it is necessary to manually move it with the conventional method, and the task is extremely difficult and time-consuming. Therefore, there is a method to generate CG animation from the feature points of the tracked face. However, face tracking technology has problems such as the inability to track if the face is hidden, and the need for large-scale equipment to track with high accuracy. In order to solve such problems, research has been conducted to generate facial expressions by using machine learning from speech linked to mouth movement. However, these studies do not take into account the individuality of the user during the learning process. In this paper, we propose a method for performing transfer learning on a trained model for the purpose of generating animations that take into account the user's individuality. Verification experiments based on the proposed method are conducted with data generated with and without transfer learning as an evaluation.
Comparative Study on the McEliece Public-Key Cryptosystem Based on Goppa and QC-MDPC Codes

In recent years, much research has been conducted on quantum computers – machine that exploit the phenomena of quantum mechanics to solve difficult or insoluble mathematical problems for conventional computers. If large-scale quantum computers are built, they will be able to break many of the public key cryptosystems currently in use. This would seriously compromise the confidentiality and integrity of digital communications on the internet. Post-quantum cryptography aims to develop secure cryptographic systems against both conventional as well as quantum computers for interacting with existing protocols and communication networks. In this paper we present a public key cryptosystem of McEliece based on the correcting codes, using two types of correcting codes; QC-MDPC and Goppa correcting codes. This latter seems very interesting considering its two characteristics, namely the power of correction and the efficient decoding algorithm which resistant to quantum attacks due to difficulty of decoding a linear code. On the other hand, QC-MDPC cryptosystem code is rapid and more secure than Goppa cryptosystem.
Automated Detection of Normal and Cardiac Heart Disease Using Chaos Attributes and Online Sequential Extreme Learning Machine

Cardiovascular diseases (CVDs) are major reason of mortality in the world population, and the numeral of cases is up surging every year. The mortality rate due to coronary artery disease (CAD) and congestive heart failure (CHF) is higher than any other type of CVDs. Therefore, an early detection and diagnosis of CAD and CHF patients are essential. For this, an automated noninvasive approach has been proposed to detect CAD and CHF patients using attributes extracted from heart rate variability (HRV) signal. The automated scheme is based on chaos attributes extracted from heart rate variability signal (HRV), dimension reduction of attributes such as Generalized Discriminant Analysis (GDA) and online sequential extreme learning machine(OSELM). For this study, the HRV database of normal sinus rhythm (NSR), CHF, and CAD subjects have been taken from physionet.org website. The numerical results have shown that GDA with Gaussian kernel function and OSELM with sine activation function achieved accuracy (AC) of 99.34% and sensitivity (SE) of 99.32% for NSR-CAD group, and AC and SE of 100% were achieved for NSR-CHF group.
Position-aware and Symmetry Enhanced GAN for Radial Distortion Correction

This paper presents a novel method based on the generative adversarial network for radial distortion correction. Instead of generating a corrected image, our generator predicts a pixel flow map to measure the pixel offset between the distorted and corrected image. The quality of the generated pixel flow map and the warped image are judged by the discriminator. As texture far away from the image center has strong distortion, we develop an Adaptive Inverted Foveal layer which can transform the deformation to the intensity of the image to exploit this property. Rotation symmetry enhanced convolution kernels are applied to extract geometric features of different orientations explicitly. These learned features are recalibrated using the Squeeze-and-Excitation block to assign different weights for different directions. Moreover, we construct a first real-world radial distorted image dataset RD600 annotated with ground truth to evaluate our proposed method. We conduct extensive experiments to validate the effectiveness of each part of our framework. The further experiment shows our approach outperforms previous methods in both synthetic and real-world datasets quantitatively and qualitatively.
Prediction of the Tuberculosis Patients Who Can Recover Normally Using a Support Vector Machine with Radial and Polynomial Kernels

Tuberculosis is a contagious disease that is generally transmitted through a sufferer's cough and is deadly. Tuberculosis usually attacks the lungs but can also affect other parts of the body. Treatment of tuberculosis patients who do not recover with those who can recover is different, mishandling can cause death in patients. Therefore, we need a system that can predict whether the patient's condition can recover normally, or the lungs cannot be recovered. Support vector machine is a learning system that uses a hypothetical linear function in a high dimensional space and is trained with an algorithm based on optimization theory by applying learning bias derived from statistical theory. In this study, the kernel function is used, namely the radial kernel and the polynomial. Based on the analysis and discussion that has been done, it can be concluded from this study that the performance of the radial and polynomial kernels is the same with an accuracy of 85% and a sensitivity value of 94%.
Urdu QA: Question Answering System for Urdu Language

Question answering provides the new method for information query. Q and A systems are able to return answers to the questions posed in natural language, and they return concise to the point answers as compared to various search engines which return set of web links containing large volumes of unwanted information. In this paper, the model for question answering system is proposed for Urdu language (UrduQA). It is the model which takes questions posed in Urdu language and attempts to provide short answers. This problem for Urdu is tackled using information retrieval and natural language processing techniques. Keyword matching is used from both question and candidate documents selected by IR phase of Urdu QA. For evaluation, experiments are conducted on EMILLE/CILL corpus.
Single Image Blind Deblurring

With the growth of technology and digital media, images have started playing a critical role not only in our day-to-day lives but also in fields like Biology and Astronomy to name a few. However, images prove to be useful only when they can successfully convey information to a viewer. Blurred images are lacking in this aspect and deblurring images to extract useful data from them is the need of the hour. Through this research, we have attempted to develop a generic solution to deblur images affected by uniform motion and focus blur. Apart from achieving the feat of being able to tackle multiple types of blur, our work has shown to be at par with its competitors in terms of the Peak Signal to Noise Ratio metric and the precision factor of the new and developing Object Detection metric.
A Graph Signal Processing Based Strategy for Deep Neural Network Inference

Deep neural networks (DNNs) have played a critical role in a variety of real-world applications, such as image processing, speech recognition and autonomous driving. For the sake of deploying DNN on embedded devices and applying in practical application scenes, parallelization is required to accelerate the inference of DNN models. However, unlike the fully connected weight matrix, the sizes of convolutional kernel and deconvolution kernel are usually too small to match the output of the previous layer, which makes convolution and deconvolution hard to parallelize. In this paper, the graph signal processing (GSP) technology is firstly introduced to adjust the convolutional and deconvolution operation for parallelization. The input of the convolutional layer is represented by graph signal, and then the convolutional kernel is transformed into the graph filter matrix by GSP technology. To further accelerate inference process, the layer fusion is adopted in this work. We have implemented our method on the Jetson embedded development board with Nvidia Unified Compute Device Architecture (CUDA). The experimental results show that the graph signal processing based DNN is valid. And it is effective in regularizing deep neural network and benefit to speed up on embedded GPU platforms.
Uniformization Stable Markov Models and Their Jordan Algebraic Structure

We provide a characterization of the continuous-time Markov models where the Markov matrices from the model can be parameterized directly in terms of the associated rate matrices (generators). That is, each Markov matrix can be expressed as the sum of the identity matrix and a rate matrix from the model. We show that the existence of an underlying Jordan algebra provides a sufficient condition, which becomes necessary for (so-called) linear models. We connect this property to the well-known uniformization procedure for continuous-time Markov chains by demonstrating that the property is equivalent to all Markov matrices from the model taking the same form as the corresponding discrete-time Markov matrices in the uniformized process. We apply our results to analyze two model hierarchies practically important to phylogenetic inference, obtained by assuming (i) time reversibility and (ii) permutation symmetry, respectively.
[DC] "SHOW YOUR DEDICATION:" VR Games and Outmersion

Videogame scholarship has noted the unhelpful vagueness of `immersion' as a descriptor, yet in the discourse surrounding VR entertainment, `immersion' remains a central focus, and is lauded by users as the primary goal of VR experiences. Building on the legacy of games scholarship that complicates `immersion' [5], [10] this study uses close-play to examine several emergent tropes in VR experiences that rely on outmersive design: VR suicide, bodily alteration of the avatar, and VR-within-VR. Ultimately, I advocate for increased attention to how VR's technical capacity for user immersion differs from the reality of many VR experiences that use outmersive design.
News text classification based on attention mechanism

News text is often divided into two parts: title and content. In the research of news text classification, many studies have not considered the characteristics of news text classification. It is unscientific to merge the title and content as input hastily. The title is of great value to news text, and its importance is ignored in text classification, which has a certain impact on the classification effect. Aiming at this problem, this article designs a word vector weighting method based on attention mechanism, and builds a bidirectional long short memory neural network (BiLSTM) model for text classification. The experimental results show that BiLSTM news text classification with the introduction of attention mechanism improves the classification effect.
Second-order Attention Guided Convolutional Activations for Visual Recognition

Recently, modeling deep convolutional activations by the global second-order pooling has shown great advance on visual recognition tasks. However, most of the existing deep second-order statistical models mainly compute second-order statistics of activations of the last convolutional layer as image representations, and they seldom introduce second-order statistics into earlier layers to better fit network topology, thus limiting the representational ability to a certain extent. Motivated by the flexibility of attention blocks that are commonly plugged into intermediate layers of deep convolutional networks (ConvNets), this work makes an attempt to combine deep second-order statistics with attention mechanisms in ConvNets, and further proposes a novel Second-order Attention Guided Network (SoAG-Net) for visual recognition. More specifically, SoAG-Net involves several SoAG modules seemingly inserted into intermediate layers of the network, in which SoAG collects second-order statistics of convolutional activations by polynomial kernel approximation to predict channel-wise attention maps utilized for guiding the learning of convolutional activations through tensor scaling along channel dimension. SoAG improves the nonlinearity of ConvNets and enables ConvNets to fit more complicated distribution of convolutional activations. Experiment results on three commonly used datasets illuminate that SoAG-Net outperforms its counterparts and achieves competitive performance with state-of-the-art models under the same backbone.
Soil Sample Assay Uncertainty and the Geographic Distribution of Contaminants: Error Impacts on Syracuse Trace Metal Soil Loading Analysis Results

A research team collected 3609 useful soil samples across the city of Syracuse, NY; this data collection fieldwork occurred during the two consecutive summers (mid-May to mid-August) of 2003 and 2004. Each soil sample had fifteen heavy metals (As, Cr, Cu, Co, Fe, Hg, Mo, Mn, Ni, Pb, Rb, Se, Sr, Zn, and Zr), measured during its assaying; errors for these measurements are analyzed in this paper, with an objective of contributing to the geography of error literature. Geochemistry measurements are in milligrams of heavy metal per kilogram of soil, or ppm, together with accompanying analytical measurement errors. The purpose of this paper is to summarize and portray the geographic distribution of these selected heavy metals measurement errors across the city of Syracuse. Doing so both illustrates the value of the SAAR software’s uncertainty mapping module and uncovers heavy metal characteristics in the geographic distribution of Syracuse’s soil. In addition to uncertainty visualization portraying and indicating reliability information about heavy metal levels and their geographic patterns, SAAR also provides optimized map classifications of heavy metal levels based upon their uncertainty (utilizing the Sun-Wong separability criterion) as well as an optimality criterion that simultaneously accounts for heavy metal levels and their affiliated uncertainty. One major outcome is a summary and portrayal of the geographic distribution of As, Cr, Cu, Co, Fe, Hg, Mo, Mn, Ni, Pb, Rb, Se, Sr, Zn, and Zr measurement error across the city of Syracuse.
COMPRESSION OF SATELLITE IMAGES USING ENHANCEDFRACTAL IMAGE COMPRESSION

In Recent days Remote Sensing becomes the most popular areas to explore the geographicalinformation. The rapid growth of remote sensing applications needs an effective and standardized imagecompression technique to process it. As the Remote Sensing Images (RSI) contain huge amount of diversegeographical information. Since the decomposition of mixed pixel degrades the success of Image Compression.Although many schemes available to speed up encoding, they do not easily satisfy the encoding time or thereconstructed image quality requirements. Fractal Geometry set a way for describing and analyzing thecomplexity of diverse features of RSI. The Fractal Image Compression (FIC) is with high compression ratio evenas maintaining a high quality of reconstructed image. But it suffers from very high computational time / costassociated with the encoding stage. In order to speed up characteristics an Enhanced FIC method incorporatingSpiral partitioning with Bio Geography Based optimization technique is employed which results in betterperformance of the practical applications.
IoT BASED AGRICULTURE 4.0: CHALLENGES AND OPPORTUNITIES

In recent years, the world's population growth has been intensifying, resulting in specific problems related to the depletion of natural resources, food shortages, declining fertile lands, and changing weather conditions. This paper has been discussed the use of IoT technology as a solution to such problems. At the same time, the emergence of IoT technology has given rise to a new research direction in agriculture. Soil analysis and monitoring using Zigbee wireless sensor network technology, which is part of the IoT, will enable the creation of an IoT ecosystem as well as the development of smart agriculture. In addition, entrepreneurship, marketing and market opportunities in agriculture will be created with the help of IoT technologies. Therefore, this paper is studied the existing problems in agriculture and suggested ways and solutions use IoT technology in overcoming them.
Application of Student achievement Analysis based on Apriori Algorithm

Today's era is an information age as well as a data age, which provides conditions for the reform and development of education. With the substantial increase in the number of school students, student performance and other data show an explosive growth trend, we now urgently need to use technology to promote teaching progress. In the era of big data, this paper applies Apriori algorithm to practice, analyzes students' performance and draws conclusions, facilitates educational management, scientifically arrange courses, improves students' learning efficiency and achieves teaching objectives.
Sense of Presence in a Virtual Reality Fashion Show 360° Video

Despite the increasing number of virtual reality (VR) adoption in varying industries including luxury brands, lack of study conducted to discuss the sense of presence in VR fashion show. This phenomenological research investigates audience's sense of presence in experiencing Dior fashion show 360°VR video. Riches' factors affecting sense of presence is used as a guideline to discuss the sense of presence. In-depth interview and observation were conducted to six participants representing young workers in fashion industry and fashion enthusiasts working on other industry. The data analysis yields eight main points: 1) The absence of music beat reducing the vibe of the event; 2) The actions of other audiences recall narrative of similar real events; 3) No interaction with the avatar leading to the feeling of being unrecognized; 4) Curiosity about the embodiment; 5) Limitation to see the detail of the clothes despite the closer look; 6) Dizziness experienced by minority; 7) Huge logo patched to cover the stitch line; 8) The compatibility of environment temperature. Findings from this qualitative research may be useful for fashion designer, brands, show organizer, and sponsor to figure out the potential usability of VR fashion show. Furthermore, these findings can be used as a reference for suitable VR implementation for fashion show and as indicator to improve VR fashion show.
Improving Image Matching with Varied Illumination

We present a method to maximize feature matching performance across stereo image pairs by varying illumination. We perform matching between views per lighting condition, finding unique SIFT correspondences for each condition. These feature matches are then collected together into a single set, selecting those features which present the highest quality match. Instead of capturing each view under each illumination, we approximate lighting changes with a pretrained relighting convolutional neural network which only requires each view captured under a single specified lighting condition. We then collect the best of these feature matches over all lighting conditions offered by the relighting network. We further present an optimization to limit the number of lighting conditions evaluated to gain a specified number of matches. Our method is evaluated on a set of indoor scenes excluded from training the network with comparison to features extracted from pretrained VGG16. Our method offers an average 5.5× improvement in number of correct matches while retaining similar precision than by the original lit image pair per scene alone.