Reviewerly/exHarmony huggingface dataset:
Compressed Sensing with Invertible Generative Models and Dependent Noise

We study image inverse problems with invertible generative priors, specifically normalizing flow models. Our formulation views the solution as the maximum a posteriori (MAP) estimate of the image given the measurements. Our general formulation allows for any differentiable noise model with long-range dependencies as well as non-linear differentiable forward operators. We establish theoretical recovery guarantees for denoising and compressed sensing under our framework. We also empirically validate our method on various inverse problems including compressed sensing with quantized measurements and denoising with highly structured noise patterns.
Review of pedestrian tracking: Algorithms and applications

Pedestrian tracking is a hotspot and a difficult topic in computer vision research. Through the tracking of pedestrians in video materials, trajectories can be extracted to support the analysis of individual or collected behavior dynamics. In this review, we first discuss the difference between pedestrian tracking and pedestrian detection. Then we summarize the development of traditional tracking algorithms and deep learning-based tracking algorithms, and introduce classic pedestrian dynamic models. In the end, typical applications, including intelligent monitoring, congestion analysis, and anomaly detection are introduced systematically. With the rising use of big data and deep learning techniques in the area of computer vision, the research on pedestrian tracking has made a leap forward, which can support more accurate, timely extraction of behavior patterns and then to facilitate large-scale dynamic analysis of individual or crowd behavior.
VisME: Visual microsaccades explorer

This work presents a visual analytics approach to explore microsaccade distributions in high-frequency eye tracking data. Research studies often apply filter algorithms and parameter values for microsaccade detection. Even when the same algorithms are employed, different parameter values might be adopted across different studies. In this paper, we present a visual analytics system (VisME) to promote reproducibility in the data analysis of microsaccades. It allows users to interactively vary the parametric values for microsaccade filters and evaluate the resulting influence on microsaccade behavior across individuals and on a group level. In particular, we exploit brushing-and-linking techniques that allow the microsaccadic properties of space, time, and movement direction to be extracted, visualized, and compared across multiple views. We demonstrate in a case study the use of our visual analytics system on data sets collected from natural scene viewing and show in a qualitative usability study the usefulness of this approach for eye tracking researchers. We believe that interactive tools such as VisME will promote greater transparency in eye movement research by providing researchers with the ability to easily understand complex eye tracking data sets; such tools can also serve as teaching systems. VisME is provided as open source software.
Trajectory planning and tracking control of unmanned ground vehicle leading by motion virtual leader on expressway

Formation driving can effectively solve the problem of driving efficiency and safety of unmanned ground vehicles (UGVs) on expressways. This study introduces a trajectory planning and tracking control method from initial position to its motion virtual leader in formation driving. In order to reduce the disturbance to the surrounding UGVs, trajectory planning is divided into two stages: steady speed tracking stage and parameter value matching stage. A tracking controller combined with a trajectory planner is designed to track the reference trajectory accurately. To ensure calculation speed and accuracy, a nonlinear model-predictive control model is used in the trajectory planner, and the tracking controller is based on the linear time-varying model-predictive control (LTV MPC) model. Simulations are carried out with Carsim and MATLAB/Simulink, and the simulation results and comparative analysis show that the planned trajectory is feasible and can effectively avoid static and moving obstacles. Compared with the Stanley controller, the LTV MPC controller has the advantages of faster convergence speed, better stability and accurately tracking.
Tracing Digital Contact Tracing: Surveillance Technology and Privacy Rights During COVID-19 in China, South Korea, and the United States

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
VIRTUALIZATION OF IDENTITY IN THE CONTEXT OF SELF-REALIZATION OF A PERSONALITY

Purpose. The research is aimed at clarifying the essence of virtual reality and its productive role in the self-realization of the individual, as well as the importance in the process of self-realization of the individual to expand the dimensions of his identity by including virtual dimensions. To do this, the process of formation of the phenomenon of virtual identity in the environment of virtual reality is revealed and the influence of productive human activity in virtual reality on the nature of virtualization of its identity is turned out. Theoretical basis of the work is understanding virtual reality as a combination of conscious productive imagination as its attribute, on the one hand, and the technological component in the form of a computer and related programs on the other hand. An anthropological prerequisite for virtual reality is the ability of the man’s creative imagination to calculate and choose the best model from many mental design ones, using, among other things, horizontal computer networks, which form a virtual identity. Originality. It was found that a necessary condition for the formation of a virtual identity is the exchange of results of productive and intermediary activities between Internet users, which they carry out in Internet networks with the help of virtual reality itself. Accordingly, philosophical studies of virtual reality (R. Burrows, G. Cooper, M. Heim, R. Harper, N. Green, J. Juul, B. Loader, N. McDonnell, N. Wildman, S. Muncer, G. M. Murtagh, S. Nettleton, O. Ollinaho, N. Pleace, G. M. P. Swann, T. P. Watts) are gradually supplemented by research in the field of virtual identity (R. Baltezarevic, B. Baltezarevic, V. Baltezarevic, D. Deh, D. Glodovic, Este N. Beck, P. Kwiatek, R. A. Hardesty, B. Sheredos, N. McDonnell, N. Wildman, O. Ollinaho, E. J. Ramirez, S. LaBarge, J. Spiegel). Competitive production and distribution of human livelihoods through creative project work in virtual reality in interaction and communication with Others in a rapidly changing society requires the expansion of identity, including virtual dimensions. In the modern world self-realization of the individual includes the expansion of identity through its virtualization. Conclusions. A person’s productive activity in computer virtual reality – as an auxiliary tool of his creative imagination –creates his virtualized identity in solidarity and competitive interaction with Others, promotes self-realization of his personality and makes his holistic identity more flexible. Accordingly, philosophical studies of virtual reality over time are supplemented by philosophical anthropology studies of virtual identity.
Ink Marker Segmentation in Histopathology Images Using Deep Learning

Due to the recent advancements in machine vision, digital pathology has gained significant attention. Histopathology images are distinctly rich in visual information. The tissue glass slide images are utilized for disease diagnosis. Researchers study many methods to process histopathology images and facilitate fast and reliable diagnosis; therefore, the availability of high-quality slides becomes paramount. The quality of the images can be negatively affected when the glass slides are ink-marked by pathologists to delineate regions of interest. As an example, in one of the largest public histopathology datasets, The Cancer Genome Atlas (TCGA), approximately $$12\%$$ of the digitized slides are affected by manual delineations through ink markings. To process these open-access slide images and other repositories for the design and validation of new methods, an algorithm to detect the marked regions of the images is essential to avoid confusing tissue pixels with ink-colored pixels for computer methods. In this study, we propose to segment the ink-marked areas of pathology patches through a deep network. A dataset from 79 whole slide images with 4, 305 patches was created and different networks were trained. Finally, the results showed an FPN model with the EffiecentNet-B3 as the backbone was found to be the superior configuration with an F1 score of $$94.53\%$$ .
FPGA‐based favourite skin colour restoration using improved histogram equalization with variable enhancement degree and ensemble extreme learning machine

This paper presents skin color enhancement based on favorite skin color to agree with user-defined favorite skin color using improved histogram equalization with variable enhancement degree (IHEwVED) and machine learning methods. The skin color to be adjusted in the input image is shifted to favorite skin color by using novel control parameters of the proposed IHEwVED method. Three different novel display device-dependent color image processing methods are introduced based on hsv and yiq color space to obtain the desired enhanced output images. A reduced convolutional neural network and the novel ensemble extreme learning machine (EELM) architectures are developed and implemented in a field-programmable gate array to test, synthesize, and validate the recognition capability of the user-defined favorite skin color. The less computational complex proposed IHEwVED-EELM method recognizes 45 to 50 favorite skin color per second of test images by consuming 0.035 second training time with training root mean square error (RMSE) of 0.0048 and testing RMSE of 0.01208. Finally, a stand-alone favorite skin color restoration system is developed using the high-speed video processor NI-PXI-1031 based on the IHEwVED-EELM method in the Python-OpenCV environment. The laboratory experimental performances ascertain the real-time ability of the proposed favorite skin color restoration method.
A Fast and Effective System for Analysis of Optokinetic Waveforms with a Low-Cost Eye Tracking Device

Optokinetic nystagmus (OKN) is an involuntary eye movement induced by motion of a large proportion of the visual field. It consists of a “slow phase (SP)” with eye movements in the same direction as the movement of the pattern and a “fast phase (FP)” with saccadic eye movements in the opposite direction. Study of OKN can reveal valuable information in ophthalmology, neurology and psychology. However, the current commercially available high-resolution and research-grade eye tracker is usually expensive. Methods &amp; Results: We developed a novel fast and effective system combined with a low-cost eye tracking device to accurately quantitatively measure OKN eye movement. Conclusions: The experimental results indicate that the proposed method achieves fast and promising results in comparisons with several traditional approaches.
K-means Clustering Analysis of Crimes on Indian Women

Violence against women is seen as sexual or physical activity committed against women. In India, general forms of violence against women in India includes cruelty by relatives, dowry, rape, sexual assault, kidnapping, immoral trafficking, molestation etc. The security of the women is the utmost priority of any government in this world. In India, many policies and laws have been enforced to ensure the safety against women. Technology is being the biggest supporter to the government in this context. Data mining allows various techniques such as clustering classification, regression provides analysis in any form of data and helps intelligent predictions on the given dataset. In this paper, we use k-means clustering analysis on women crime dataset. As a part of pre-processing, we collated the data entries which had crime cases against women and made women crime sub-dataset from the real dataset. We then applied K means clustering for further analysis. We used a rapid miner tool for clustering analysis as it is widely used for clustering purposes. After completion of clustering analysis, we proposed our views and discussions on the clustering results. At the end, we ended up giving the futuristic work to be further done on the derived dataset we made and made available on public repositories.
Counting Small Permutation Patterns

A sample of n generic points in the xy-plane defines a permutation that relates their ranks along the two axes. Every subset of k points similarly defines a pattern, which occurs in that permutation. The number of occurrences of small patterns in a large permutation arises in many areas, including nonparametric statistics. It is therefore desirable to count them more efficiently than the straightforward ~O(n^k) time algorithm. This work proposes new algorithms for counting patterns. We show that all patterns of order 2 and 3, as well as eight patterns of order 4, can be counted in nearly linear time. To that end, we develop an algebraic framework that we call corner tree formulas. Our approach generalizes the existing methods and allows a systematic study of their scope. Using the machinery of corner trees, we find twenty-three independent linear combinations of order-4 patterns, that can be computed in time ~O(n). We also describe an algorithm that counts one of the remaining 4-patterns, and hence all 4-patterns, in time ~O(n^(3/2)). As a practical application, we provide a nearly linear time computation of a statistic by Yanagimoto (1970), Bergsma and Dassios (2010). This statistic yields a natural and strongly consistent variant of Hoeffding's test for independence of X and Y, given a random sample as above. This improves upon the so far most efficient ~O(n^2) algorithm.
Human or Neural Translation?

Deep neural models tremendously improved machine translation.In this context, we investigate whether distinguishing machine from human translations is still feasible.We trained and applied 18 classifiers under two settings: a monolingual task, in which the classifier only looks at the (French) translation; and a bilingual task, in which the source text (in English) is also taken into consideration.We report on extensive experiments involving 4 neural MT systems (Google Translate, DeepL, as well as two systems we trained) and varying the domain of texts.We show that the bilingual task is the easiest one and that transfer-based deep-learning classifiers perform best, with mean accuracies around 85% in-domain and 75% out-of-domain.
Exploring Design Strategies for Augmented Reality Learning Experience in Classrooms

The use of ICT mediums is prevalent in schools nowadays. Augmented Reality (AR) is an emerging technology that can provide fun and learner-centric ways of teaching in classrooms. The objective thus lie in the appropriate translation of the textbook content to support learning through AR using tablets or mobiles in the classroom. In the process, it's also required to define the smooth transition from instructor-mediated to learner-centric design for providing AR as a teaching-aid. The research thus focuses on identifying the design strategies and considerations in the form of a framework that are required to design such AR learning experiences for middle school students in a classroom scenario.
Rdataretriever: R Interface to the Data Retriever

The rdataretriever provides an R interface to the Python-based Data Retriever software.The Data Retriever automates the multiple steps of data analysis including downloading, cleaning, standardizing, and importing datasets into a variety of relational databases and flat file formats.It also supports provenance tracking for these steps of the analysis workflow by allowing datasets to be committed at the time of installation and allowing them to be reinstalled with the same data and processing steps in the future.Finally, it supports the installation of spatial datasets into relational databases with spatial support.The rdataretriever provides an R interface to this functionality and also supports importing of datasets directly into R for immediate analysis.The system also supports the use of custom data processing routines to support complex datasets that require custom data manipulation steps.The Data Retriever and rdataretriever are focused on scientific data applications including a number of widely used, but difficult to work with, datasets in ecology and the environmental sciences.
Machine Translation for English–Inuktitut with Segmentation, Data Acquisition and Pre-Training

Translating to and from low-resource polysynthetic languages present numerous challenges for NMT. We present the results of our systems for the English--Inuktitut language pair for the WMT 2020 translation tasks. We investigated the importance of correct morphological segmentation, whether or not adding data from a related language (Greenlandic) helps, and whether using contextual word embeddings improves translation. While each method showed some promise, the results are mixed.
Evaluating the Coding Performance of 360° Image Projection Formats Using Objective Quality Metrics

Recently, 360° content has emerged as a new method for offering real-life interaction. Ultra-high resolution 360° content is mapped to the two-dimensional plane to adjust to the input of existing generic coding standards for transmission. Many formats have been proposed, and tremendous work is being done to investigate 360° videos in the Joint Video Exploration Team using projection-based coding. However, the standardization activities for quality assessment of 360° images are limited. In this study, we evaluate the coding performance of various projection formats, including recently-proposed formats adapting to the input of JPEG and JPEG 2000 content. We present an overview of the nine state-of-the-art formats considered in the evaluation. We also propose an evaluation framework for reducing the bias toward the native equi-rectangular (ERP) format. We consider the downsampled ERP image as the ground truth image. Firstly, format conversions are applied to the ERP image. Secondly, each converted image is subjected to the JPEG and JPEG 2000 image coding standards, then decoded and converted back to the downsampled ERP to find the coding gain of each format. The quality metrics designed for 360° content and conventional 2D metrics have been used for both end-to-end distortion measurement and codec level, in two subsampling modes, i.e., YUV (4:2:0 and 4:4:4). Our evaluation results prove that the hybrid equi-angular format and equatorial cylindrical format achieve better coding performance among the compared formats. Our work presents evidence to find the coding gain of these formats over ERP, which is useful for identifying the best image format for a future standard.
Moving Object Detection Algorithm based on Improved Visual Background Extractor

Abstract Aiming at the problems that the ViBe algorithm causes error detection when the camera jitter, ghosts appear when processing moving targets, and the detected moving targets are incomplete, an improved ViBe algorithm based on motion compensation is proposed. In the background modeling stage, the motion compensation method based on KLT is used to obtain background model to enhance the robustness of the algorithm to the dynamic background. In the foreground detection stage, combined the background model of the current pixel with 8 neighbor pixel background model, by the double discriminant algorithm to eliminate ghost areas caused by detecting the real background points as foreground points, introduce OTSU algorithm to obtain the optimal threshold for foreground detection. In the post processing stage, filter the connected components for segmentation masks and update models to limit the wrong background points scattered in the foreground.Finally, the connected domain is repaired by morphological opening operation. The results show that the algorithm can effectively eliminate the interference caused by camera jitter, and suppress the ghost phenomenon to obtain more accurate foreground images.
Fuzzy Clustering Ensemble Considering Cluster Dependability

Clustering ensemble has been progressively popular in the ongoing years by combining several base clustering methods into a most likely better and increasingly robust one. Nonetheless, fuzzy clustering dependability (durability) has been unnoticed within the majority of the proposed clustering ensemble approach. This makes them weak against low-quality fuzzy base clusters. In spite of a few endeavors made to the clustering methods, it appears that they consider each base-clustering separately without considering its local diversity. In this paper, to compensate for the mentioned weakness a new fuzzy clustering ensemble approach has been proposed using a weighting strategy at fuzzy cluster level. Indeed, each fuzzy cluster has a contribution weight computed based on its reliability (dependability/durability). After computing fuzzy cluster dependability (reliability/durability), dependability based fuzzy cluster-wise weighted matrix (DFCWWM) is computed. As a final point, the final clustering is obtained by applying the FCM traditional clustering algorithm over DFCWWM. The time complexity of the proposed approach is linear in terms of the number of data-points. The proposed approach has been assessed on 15 various standard datasets. The experimental evaluation has indicated that the proposed method has better performance than the state-of-the-art methods.
Perception analysis of the Indonesian society on twitter social media on the increase in BPJS kesehatan contribution in the Covid 19 pandemic era

Abstract In the conditions of the Covid-19 Pandemic, the president of Indonesia, Ir Joko Widodo, on May 13, 2020, announced an increase in BPJS Kesehatan contribution. This announcement made everyone in the community boisterous, including Indonesians on social media twitter. Currently, Indonesia is ranked fifth in the world using social media twitter, so data mining on Twitter is a good opportunity to see the public’s response to a developing issue. This paper will discuss perceptions analysis of the Indonesian people on social media regarding the issue of increasing the contribution of BPJS Kesehatan. The issue of increasing the contribution of BPJS Kesehatan has been a topic of conversation on Twitter for a long time. After 30 days of data crawling, 145,359 tweets were obtained. This amount of data proves that the Indonesian people are very active in issuing opinions regarding the issue of increasing contribution BPJS Kesehatan. Various kinds of differences of opinion in each conversation are classified into three types of opinion using the Naive Bayes method. The three types of opinions are classified into positive opinions, negative opinions, and neutral opinions. The opinion classification results obtained were 73% containing negative opinions, 18% positive opinions, and 9% neutral opinions. This can serve as an early warning for the government to see the public’s response in every policy taken. So that each policy can be evaluated for the better.