Reviewerly/exHarmony huggingface dataset:
Improving GNSS Positioning by De-noising Consecutive Correlator Outputs Using Graph Fourier Transform Filtering

Navigation and positioning with GNSS are ubiquitous in our daily lives. However, GNSS receivers are vulnerable to irregular incoming signals, so their positioning, navigation, and timing (PNT) performance are usually degraded, especially in urban areas. It is, therefore, essential to enhance the baseband of GNSS receivers to sufficiently adapt to and be robust to volatile environments. Correlator outputs are the elementary products from GNSS baseband signal processing. The navigation and positioning accuracy is highly related to the quality of the correlator outputs, which contain less sophisticated noise sources than the code and carrier errors produced by the traditional tracking loops. In that case, the baseband processor can estimate the actual time of arrival (TOA) more easily. In this work, we directly de-noise the complex correlator outputs in the consecutive time domain to produce more accurate pseudorange measurements in harsh environments. Graph signal processing (GSP) is more extraordinary in alleviating irregular noise power than traditional digital signal processing (DSP). Thus, the GSP is applied to optimize the raw graph signals formed with the correlator outputs varying with the time and code offset. Then, the irregular graph domain is processed with graph Fourier transform (GFT) by exploring the geometry structure of the network of correlator outputs. The proposed GFT filtering method for the consecutive complex correlator outputs is realized in a GNSS software-defined radio (SDR) processing the GPS L1 C/A signals. Then, static GPS intermediate frequency (IF) data are collected in an urban area to test the proposed SDR. The real-world experiments demonstrate that the baseband processing results can be de-noised more efficiently, and the positioning accuracy is improved by 65.3% compared to the traditional algorithm.
Recurrent Approximation in the Tasks of the Neural Network Synthesis for the Control of Process of Phototherapy

The chapter reveals the problem of electromagnetic wave (EMW) interaction in the case when the electron of radical transition. The task of interaction between photon and electron with three natural frequencies different in three directions for terminal enzyme of respiratory chain cytochrome-c-oxidase or other photo acceptors was considered and solved. The solution of problems of encoding and decoding of polarized light interferogram data and calculation of radiation dose for different spectral composition are proposed. The examples of problem of analytical learning as standard elements for design multilayers artificial neural network (ANN) with any complicated configuration of input–output vector-function are considered. The vector-indicator was introduced and a new approximation–expansion within these three levels digital quantities was proposed. Recurrent approximation was applied for synthesis of recurrent artificial neural network (RANN). Solutions for synaptic weight coefficients (SWC) as recurrent sequence are represented in analytic form. Synthesis of control system as mini–max problem for efficiency optimization was solved. Convergence of recurrent approximation for two schemes of approach by a linear and quadratic curve are proposed and discussed for System Support Decision Making (SSDM) of physiotherapy with ANN and RANN.
Fundamental Limits for Learning Hidden Markov Model Parameters

We study the frontier between learnable and unlearnable hidden Markov models (HMMs). HMMs are flexible tools for clustering dependent data coming from unknown populations. The model parameters are known to be fully identifiable (up to label-switching) without any modeling assumption on the distributions of the populations as soon as the clusters are distinct and the hidden chain is ergodic with a full rank transition matrix. In the limit as any one of these conditions fails, it becomes impossible in general to identify parameters. For a chain with two hidden states we prove nonasymptotic minimax upper and lower bounds, matching up to constants, which exhibit thresholds at which the parameters become learnable. We also provide an upper bound on the relative entropy rate for parameters in a neighbourhood of the unlearnable region which may have interest in itself.
Performance of diagnostic tests based on continuous bivariate markers

In medical diagnostic research, it is customary to collect multiple continuous biomarker measures to improve the accuracy of diagnostic tests. A prevalent practice is to combine the measurements of these biomarkers into one single composite score. However, incorporating those biomarker measurements into a single score depends on the combination of methods and may lose vital information needed to make an effective and accurate decision. Furthermore, a diagnostic cut-off is required for such a combined score, and it is difficult to interpret in actual clinical practice. The paper extends the classical biomarkers’ accuracy and predictive values from univariate to bivariate markers. Also, we will develop a novel pseudo-measures system to maximize the vital information from multiple biomarkers. We specified these pseudo-and-or classifiers for the true positive rate, true negative rate, false-positive rate, and false-negative rate. We used them to redefine classical measures such as the Youden index, diagnostics odds ratio, likelihood ratios, and predictive values. We provide optimal cut-off point selection based on the modified Youden index with numerical illustrations and real data analysis for this paper's newly developed pseudo measures.
MoonBuddy: A Voice-based Augmented Reality User Interface That Supports Astronauts During Extravehicular Activities

As NASA pursues Artemis missions to the moon and beyond, it is essential to equip astronauts with the appropriate human-autonomy enabling technology necessary for the elevated demands of lunar surface exploration and extreme terrestrial access. We present MoonBuddy, an application built for the Microsoft HoloLens 2 that utilizes Augmented Reality (AR) and voice-based interaction to assist astronauts in communication, navigation, and documentation on future lunar extravehicular activities (EVAs), with the goal of reducing cognitive load and increasing task completion. User testing results for MoonBuddy under simulated lunar conditions have been positive overall, with participants indicating that the application was easy to use and helpful in completing the required tasks.
Uncertainty Estimation With Neural Processes for Meta-Continual Learning

The ability to evaluate uncertainties in evolving data streams has become equally, if not more, crucial than building a static predictor. For instance, during the pandemic, a model should consider possible uncertainties such as governmental policies, meteorological features, and vaccination schedules. Neural process families (NPFs) have recently shone a light on predicting such uncertainties by bridging Gaussian processes (GPs) and neural networks (NNs). Their abilities to output average predictions and the acceptable variances, i.e., uncertainties, made them suitable for predictions with insufficient data, such as meta-learning or few-shot learning. However, existing models have not addressed continual learning which imposes a stricter constraint on the data access. Regarding this, we introduce a member meta-continual learning with neural process (MCLNP) for uncertainty estimation. We enable two levels of uncertainty estimations: the local uncertainty on certain points and the global uncertainty <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p(\boldsymbol {z})$ </tex-math></inline-formula> that represents the function evolution in dynamic environments. To facilitate continual learning, we hypothesize that the previous knowledge can be applied to the current task, hence adopt a coreset as a memory buffer to alleviate catastrophic forgetting. The relationships between the degree of global uncertainties with the intratask diversity and model complexity are discussed. We have estimated prediction uncertainties with multiple evolving types including abrupt/gradual/recurrent shifts. The applications encompass meta-continual learning in the 1-D, 2-D datasets, and a novel spatial–temporal COVID dataset. The results show that our method outperforms the baselines on the likelihood and can rebound quickly even for heavily evolved data streams.
Tracelet Hopf Algebras and Decomposition Spaces (Extended Abstract)

Tracelets are the intrinsic carriers of causal information in categorical rewriting systems.In this work, we assemble tracelets into a symmetric monoidal decomposition space, inducing a cocommutative Hopf algebra of tracelets.This Hopf algebra captures important combinatorial and algebraic aspects of rewriting theory, and is motivated by applications of its representation theory to stochastic rewriting systems such as chemical reaction networks.
A deep learning based stereo matching model for autonomous vehicle

&lt;p&gt;&lt;span lang="EN-US"&gt;Autonomous vehicle is one the prominent area of research in computer vision. In today’s AI world, the concept of autonomous vehicles has become popular largely to avoid accidents due to negligence of driver. Perceiving the depth of the surrounding region accurately is a challenging task in autonomous vehicles. Sensors like light detection and ranging can be used for depth estimation but these sensors are expensive. Hence stereo matching is an alternate solution to estimate the depth. The main difficulties observed in stereo matching is to minimize mismatches in the ill-posed regions, like occluded, texture less and discontinuous regions. This paper presents an efficient deep stereo matching technique for estimating disparity map from stereo images in ill-posed regions. The images from Middlebury stereo data set are used to assess the efficacy of the model proposed. The experimental outcome dipicts that the proposed model generates reliable results in the occluded, texture less and discontinuous regions as compared to the existing techniques.&lt;/span&gt;&lt;/p&gt;
Road Surface Detection for Autonomous Smart Wheelchair

Today's wheelchair technology is evolving, with options ranging from manual wheelchairs, electric wheelchairs to the latest smart wheelchairs. However, wheelchairs on the market today still lack of the resources to facilitate advanced security for their users, particularly from the risk of injury or death caused by user neglect to differences in the rough and uneven texture surface structure and distinctions in road height levels when crossed by wheelchairs. Which would be at risk of accidents such as the existence of descending stairs. The victim may sustain physically visible physiological effects because of the accident, such as abrasions, bruises, tears, fractures, head injuries, and even death in fatal cases. The goal of this research is to enhance the security and safety of smart wheelchairs by developing autonomous controls with the extraction of gray level cooccurrence matrix (GLCM) texture features and the Naive Bayes Classification in anticipation of irregular road conditions and the existence of levels that are at risk of endangering wheelchair users based on camera input. According to the results of 100 experiments using 2-dimensional imagery data tested at d = 1, 2, 3, 4 and θ = 0°, 45°, 90°, 135° values, the resulting d = 1 and θ = 0° levels have the highest accuracy of 87% when classifying images of descending stairs and floors.
An Efficient Digital Short Signature Scheme using CCM for HC-IoT Environments

With billions of life-improving applications proliferating the global space, the Human-Centered Internet of Things (HC-IoT) grips excellent promise. The Internet will comprise more than 50 billion linked things, comprising mobile devices, GPS devices, sensors, actuators, and all the smart things that can be imagined. The HC-IoT comprises many smart gadgets to distribute identified information via online platforms. The goal is to encrypt messages between the sender and receiver during data exchange with the right instruction to challenge any human malicious attack. Different signature-based schemes have been proposed to ensure secure communication of critical user information. Smart gadgets claim lightweight characteristics, posing remarkable strengths and safety. The core challenge with signature-based techniques is the incurred computational cost owing to the enormous resources required for signature and verification procedures. The current paper projects an efficient Digital Short Signature (DSS) Scheme that uses Conformal Chaotic Maps (CCM) for protected communication in HC-IoT-based smart devices. The security of the projected scheme is thoroughly connected to a CCM in the random oracle model (ROM). The proposed DSS-CCM scheme offers more security assurance than the existing signature systems. The presented scheme would find practical applications in smart cards and emerging wireless security systems.
Transform an Electric Golf Cart into an Autonomous Vehicle

This article describes the transition from a golf cart to an autonomous vehicle. The purpose of the research is to develop a low-cost electric vehicle that can operate autonomously to carry people in environments that are not too large and have low traffic such as university campuses, resorts, amusement parks, factories, etc. The other goal of the research is that the system can work functionally with a small amount of power consumption. The system has an MCU to get obstacle sensor data to help the vehicle operate in a safe condition. The main sensor is a vision-based camera connected to the Jetson Tx2 board that helps the vehicle detect drivable space to run following the requested route.
Work-in-Progress: Object Detection Acceleration Method by Improving Execution Efficiency of AI Device

In recent years, there has been a growing need to perform object detection at the edge. Since the edge environment has tight physical constraints, the efficient use of AI devices is a key challenge to execute object detection at high throughput. In this paper, we propose an object detection acceleration method which uses two types of one-stage detectors in combination. After detecting object candidates by a lightweight detector, the method generates aggregated images by combining the candidate images and executes the second more accurate detector on the aggregated images to improve execution efficiency of AI devices. Our evaluations confirmed that the proposed method can speed up object detection by up to eight times for a license plate detection task with almost no accuracy degradation. We conducted evaluations with a car detection task and a pose estimation task as well and confirmed the broad applicability of the proposed method.
Human Interpretable Radar Through Deep Generative Models

Imaging radars are a powerful new sensor aimed at the autonomous vehicle market. With high angular resolution and dynamic range, such radars can discern between different adjacent objects, even when those are stationary. The output of the radar is a complex Point Cloud (PC) that is difficult for humans to understand and recognize objects. We investigate the use of machine learning to transform the radar PC to an interpretable format that can be understood intuitively. We employ two different generative models to transform radar PCs into synthetic LiDAR PCs and camera images of the scene. Our results demonstrate the imaging radar's ability to recognize objects such as pedestrians, parked vehicles, trees and road edges. We show how these tools can be used to analyse and evaluate the radar PC.
Multiple Pedestrian Tracking Framework using Deep Learning-based Multiscale Image Analysis for Stationary-camera Video Surveillance

A novel single static-camera multiple pedestrian detection and tracking system, which could be succesfully used by the Smart City technologies, is introduced in this article. The moving person detection process is performed by applying a combination of advanced computer vision and machine learning solutions, such as Gaussian Mixture Models (GMM), Histogram of Oriented Gradients (HOG), Support Vector Machines (SVM) and Aggregate Channel Features (ACF), to each frame of the color video sequence. An instance matching-based tracking technique that uses a deep learning-based multiscale analysis of the subimages of the detected pedestrians is then proposed. Its scale-space is created by applying the numerical approximation algorithm of a well-posed nonlinear anisotropic diffusion-based model that is introduced here. The results of the pedestrian detection and tracking experiments are described in the end.
Systematic Literature Review on Requirement Management Tools

The requirement management is a process used to manage project requirements. Requirement management tools help the organization in many ways like it reduces budget issues. Basic requirement management tools are Microsoft word, spreadsheets etc. complex requirement management tools may be a whole software system that will be used for managing the interaction among different requirements and to identify the impact of changes. For good requirements we need management tools, and these requirements leads better products and solutions of our problems. It is known that requirements which are not well understood and unrestricted changes to the scope of requirements leads to numerous software projects to failure. So, good requirements are very important for project success. Paper is about analyzing the importance of requirement management tools. One thing is understood that if requirements of the system are not best understood then they will lead towards project failure. Many professional people who are developing the projects sometimes choose wrong requirement management tools for wrong reasons. The aim of this paper is to examine types and benefits of different requirement management tools and when to use these tools. Requirement management tools are categorized depending upon their use and features. This will helpful developers as well people who use these tools. Lastly, we conclude that Objectiver is the best tool among these all. With the help of this tool stakeholder get a broader view of system.
Cache-Aided Multi-Access Multi-User Private Information Retrieval

In this paper, we consider the multi-access cache-aided multi-user Private Information Retrieval (MuPIR) problem. In this problem, N files are replicated across S servers. There are K users and C cache nodes, each capable of storing M files. Each user can access L cache nodes, and every cache node can be accessed by several users. Each user wants to retrieve one file from the servers, but users don’t want the servers to know their demands. This problem is an extension of the dedicated cache-aided MuPIR problem, which itself generalizes the single-user PIR setup. In this paper, we propose an order optimal MuPIR scheme that utilizes a multi-access setup of the coded caching problem where every set of L caches is accessed by one user resulting in K=$\begin{pmatrix} c\\ L \end{pmatrix}$. We also propose
Beyond the Blue Sky of Multimodal Interaction: A Centennial Vision of Interplanetary Virtual Spaces in Turn-based Metaverse

Human habitation across multiple planets requires communication and social connection between planets. When the infrastructure of a deep space network becomes mature, immersive cyberspace, known as the Metaverse, can exchange diversified user data and host multitudinous virtual worlds. Nevertheless, such immersive cyberspace unavoidably encounters latency in minutes, and thus operates in a turn-taking manner. This Blue Sky paper illustrates a vision of an interplanetary Metaverse that connects Earthian and Martian users in a turn-based Metaverse. Accordingly, we briefly discuss several grand challenges to catalyze research initiatives for the 'Digital Big Bang' on Mars.
The Effect of Spatial and Temporal Occlusion on Word Level Sign Language Recognition

Driven by the appeal of real-world applicable models, we investigate how temporal and spatial occlusion affect sign language recognition. Utilizing only a crop of the hands and pose flow, we maintain accuracies comparable to an I3D baseline for the WLASL dataset using a video transformer model (VTN), implying that hand crops might contain enough information for accurate prediction. Moreover, we find that a crop of only the right hand provides enough data to train an accurate model, achieving results of 0.2% less than the baseline for AUTSL and 4.7% less across all WLASL datasets. Sampling a video every fifth frame achieves comparative results to baseline, with 8 frame sequences performing better for AUTSL (0.4% less than baseline) and 16 frames performing better for WLASL (0.2% for WLASL 100 and 300). Our results indicate the feasibility of utilizing less information for sign language recognition, however more research is necessary to apply these findings in real-world scenarios.
Mapping dreams in a computational space: A phrase-level model for analyzing Fight/Flight and other typical situations in dream reports

This article demonstrates that an automated system of linguistic analysis can be developed – the Oneirograph – to analyze large collections of dreams and computationally map their contents in terms of typical situations involving an interplay of characters, activities, and settings. Focusing the analysis first on the twin situations of fighting and fleeing, the results provide densely detailed empirical evidence of the underlying semantic structures of typical dreams. The results also indicate that the Oneirograph analytic system can be applied to other typical dream situations as well (e.g., flying, falling), each of which can be computationally mapped in terms of a distinctive constellation of characters, activities, and settings.
Approaches for Documentation in Continuous Software Development

It is common practice for practitioners in industry as well as for ICT/CS students to keep writing – and reading ¬– about software products to a bare minimum. However, refraining from documentation may result in severe issues concerning the vaporization of knowledge regarding decisions made during the phases of design, build, and maintenance. In this article, we distinguish between knowledge required upfront to start a project or iteration, knowledge required to complete a project or iteration, and knowledge required to operate and maintain software products. With `knowledge', we refer to actionable information. We propose three approaches to keep up with modern development methods to prevent the risk of knowledge vaporization in software projects. These approaches are `Just Enough Upfront' documentation, `Executable Knowledge', and `Automated Text Analytics' to help record, substantiate, manage and retrieve design decisions in the aforementioned phases. The main characteristic of `Just Enough Upfront' documentation is that knowledge required upfront includes shaping thoughts/ideas, a codified interface description between (sub)systems, and a plan. For building the software and making maximum use of progressive insights, updating the specifications is sufficient. Knowledge required by others to use, operate and maintain the product includes a detailed design and accountability of results. `Executable Knowledge' refers to any executable artifact except the source code. Primary artifacts include Test Driven Development methods and infrastructure-as-code, including continuous integration scripts. A third approach concerns `Automated Text Analysis' using Text Mining and Deep Learning to retrieve design decisions.