Reviewerly/exHarmony huggingface dataset:
A radar pulse train deinterleaving method for missing and short observations

Radar pulse train deinterleaving is a key aspect of electronic support measurement systems. Identifying correct hidden periods is a prerequisite for completing the subsequent signal processing with high quality. Consequently, according to the sparsity of the interleaved radar pulse train, a new radar mixed pulse train deinterleaving algorithm based on the extended Farey dictionary and improved generalized orthogonal matching pursuit (igOMP) is proposed. This method is particularly suitable for the interleaving of the short and highly interleaved missing pulse train in complex electromagnetic environments. In addition, a novel threshold criterion is proposed to precisely extract hidden periods of the interleaved pulse train in the presence of pseudo-peaks. The simulation results demonstrate that the proposed algorithm improves the estimation accuracy of the hidden period and is robustness to the short, staggered radar interleaved pulse train with a large number of missing pulses.
GPPU: A 330.4-μJ/ task Neural Path Planning Processor with Hybrid GNN Acceleration for Autonomous 3D Navigation

A graph neural network (GNN)-based neural path planning processor, GPPU, is proposed for 3D navigation on mobile platforms. It realizes high-speed and energy-efficient path planning by using the hierarchical path planning (HPP) with three key features: 1) graph generation core (GGC) with structured dynamic resolution sampling (SDRS) and K-NN node grouping (KNG) block for GNN preprocessing, 2) reordered grid prefetcher (RGP) and hybrid diagonal-densedistributed matrix $(\mathrm{D}^{3}\mathrm{M})$ aggregation architecture for fast and efficient GNN acceleration, 3) low-latency core cluster using GNN layer fusion (GLF) for external memory access (EMA) reduction. The GPPU is fabricated in a 28 nm CMOS process and successfully demonstrates in the 3D path planning application with 1.79 ms latency and $330.4 \mu \mathrm{J} /$ task energy efficiency at 0.9 V, 200 MHz operation condition.
On Natural Language Processing to Attack COVID-19 Pandemic: Experiences of Vietnam

The world health system is under tremendous pressure from COVID-19 pandemic, when millions of people are infected every day. Therefore, finding solutions to support the health sector is the most necessary issue today. Artificial intelligence has greatly supported the healthcare system by supporting many automated tasks. In this chapter, we want to introduce a system that automatically extracts the necessary information from medical documents (also known as sequence tagging or named entity recognition in the field of natural language processing), helping the medical team save time and effort from reading comprehension and extracting information manually. It will be shown that the system’s contributions to the containment of the COVID-19 pandemic are in practice in Vietnam.
HSKP-CF Algorithm Based on Target Tracking for Mobile Following Robot

Aiming at the human target tracking problem of mobile robot in real scene, a Correlation Filtering algorithm that integrates information of the Human Skeleton Key Points (HSKP-CF) is proposed. Based on the excellent performance of HOG and CN feature operators in dealing with target deformation and illumination, these two features in correlation filtering algorithm are fused to improve the performance of the tracker in the above two situations. HSKP-CF includes a filtering mechanism based on the key point information of the human body, and combined with the correlation filtering algorithm to make the model sensitive to the key point information of the human body while paying attention to the features selected by the Kernelize Correlation Filtering (KCF) tracker. Finally, a total of 6670 frames of video image sequences collected on the mobile robot platform are used for algorithm verification and comparative analysis. The outdoor tracking accuracy of the HSKP-CF exceeds the traditional KCF by 46.4% and reaches 93.6%, which shows the effectiveness of the method. Follow-up research needs to reduce the calculation of the model and lay a foundation for the deployment on mobile devices.
Predicting Critically Ill Patients Short-Term Mortality Risk Using Routinely Collected Data: Deep Learning Model Development, Validation, and Explanation

This paper aims to develop and validate a deep learning-based short-term mortality risk prediction model for critically ill patients by using routinely collected data in a large Chinese cohort and explore the explainability of the model decision.A total of 10925 critically ill patients between January 2014 and June 2020 are included in this study.Data routinely collected in the electronic health records (EHRs) system are extracted and used to develop a short-term mortality risk prediction model based on a deep artificial neural network (ANN).The features include demographic characteristics, vital signs, laboratory tests, and the daily dose of intravenous medications.The developed deep learning model (AUROC: 0.88, AUPRC: 0.63, Brier score: 0.108) is superior to the model based on APACHE II scores (AUROC: 0.78, AURPC: 0.52, Brier score: 0.124) in the prediction of hospital mortality for
Quantum-defended Digital Signature on Lattice for IoT-enabled Systems

Information exchange through unsecured platforms (including various sorts of documents, photos, and videos) is common nowadays since these platforms are frequently utilized in people’s daily lives. The Internet of Things (IoT) has become a growing technology, and authentication, integrity, and nonrepudiation are three typical security criteria in the case of data communications within IoT. A typical solution is to use a cryptographic mechanism called a digital signature. Recently, it has been seen that the security of the existing signature schemes can be broken as a result of futuristic quantum computers. As a response, we were inspired to concentrate on more sophisticated and challenging lattice assumptions that might withstand the new advanced technologies such as quantum attacks. In this paper, we design a lattice-based digital signature scheme for IoT-enabled systems which resists the existing as well as futuristic quantum attacks. The security of the proposed scheme is based on the lattice small integer solution (SIS) and in-homogeneous SIS (ISIS) problems. These lattice problems provide very strict security proofs based on worst-case hardness. A well robust security model is also provided to confirm the security of the proposed signature scheme.
An Enhanced DES Algorithm with Secret Key Generation-Based Image Encryption

Encryption technique is one of the major approaches for the security of the data in networking zone and intranet. Every type of data has its own characteristics; consequently, to safeguard private picture data from unwanted access, a variety of strategies are employed. In this paper, an image encryption technology called data encryption standard (DES) is combined with XOR to create a block cipher transformation algorithm for picture security. The suggested method is based on DES with XOR encryption that based on the concept of pixel randomization in RGB combination. The findings of the suggested method indicate more variety. The security of the system will be increased by increasing the variety.
Entanglement structures in quantum field theories. II. Distortions of vacuum correlations through the lens of local observers

When observing a quantum field via detectors with access to only the mixed states of spatially separated, local regions---a ubiquitous experimental design---the capacity to access the full extent of distributed entanglement can be limited, shrouded by classical correlations. By performing projective measurements of the field external to two detection patches and classically communicating the results, underlying pure states may be identified for which entanglement quantification is clear. In the Gaussian continuous-variable states of the free scalar field vacuum, this protocol uncovers a disparity between the spacelike entanglement established within the field and that which is locally detectable. This discrepancy is found to grow exponentially with the separation between observation regions. The protocol developed herein offers insight and practical guidance for clarifying the unavoidable distortion of quantum field correlations when viewed from the vantage of a pair of local observers.
Approximate quantum error correction, covariance symmetry, and their relation

To perform reliable quantum computation, quantum error correction is indispensable. In certain cases, continuous covariance symmetry of the physical system can make exact error correction impossible. In this work we study the approximate error correction and covariance symmetry from the information-theoretic perspective. For general encoding and noise channels, we define a quantity named infidelity to characterize the performance of the approximate quantum error correction and quantify the noncovariance of an encoding channel with respect to a general Lie group from the asymmetry measure of the corresponding Choi state. In particular, when the encoding channel is isometric, we derive a trade-off relation between infidelity and noncovariance. Furthermore, we calculate the average infidelity and noncovariance measure for a type of random code.
Novel Application of Furniture Product Using Augmented Reality

A tool for individualized consumer pleasure is augmented reality, particularly when it comes to the interior and exterior of furniture. Customers truly want to be able to visualise how the products will appear in their homes or places of business. The consumer or user wants to visualise virtual content for interior design concepts in real time, which AR enables. This study suggests a novel method for integrating augmented reality (AR) technology into interior design, allowing users to share information about 3D virtual furniture and view it on a flexible, dynamic user interface. One way to think of augmented reality technology is as a blend of actual space and virtual items. In an AR environment, the user can observe and modify the virtual furniture in real-time on the screen.
Research on far-field spot search and centre location algorithms

The energy distribution of the far-field spot is uneven, and the background is complex. Therefore, the identification of far-field points and the positioning of the centre are difficult. This article proposes an algorithm for multi-scale Gaussian cyclic convolution for locating the centre of far-field spots. First, wavelet denoising is performed on the image of multiple far-field spots, and the images of adjacent frames are subtracted. Then, the absolute values of the differences are added. Due to the large size of the far-field spot, the Gaussian distribution of the laser energy is not obvious. Therefore, a multi-scale Gaussian convolution kernel is used to perform circular convolution on these images. To preserve low-scale Gaussian features, features are spliced between two convolutions. In this article, we also design multi-angle combination filters to filter the enhanced Gaussian distribution features. Finally, curved polynomials are fitted to the reconstructed Gaussian energy distribution to obtain the maximum value; at this point, the position where the maximum value lies is the centre of the far-field spot. The experiments showed that this method has better robustness.
Real-time Multi-module Student Engagement Detection System

We present a method to aggregate four different facial cues to help identify distraction among online learners: facial emotion detection, micro-sleep tracking, yawn detection, and iris distraction detection. In our proposed method, the first module identifies facial emotions using both 2D and 3D convolutional neural networks (CNNs) which facilitates comparison between spatiotemporal and solely spatial features. The other three modules use a 3D facial mesh to localize the eye and lip coordinates in order to track a student’s facial landmarks and identify iris positions as well as signs of micro-sleep like yawns or drowsiness. The results from each module are combined to form an all-encompassing label displayed on an integrated user interface that can further be used to provide real-time alerts to students and instructors when required. From our experiments, the emotion, micro-sleep, yawn, and iris monitoring modules individually achieved 72.5%, 95%, 97%, and 93% accuracy scores, respectively.
ANALYSIS OF DATA ENGINEERING FOR FRAUD DETECTION USING MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE TECHNOLOGIES

Because identifying possibly fraudulent transactions is a binary classification issue, several methods may be utilized to achieve this objective.Machine learning Artificial intelligence and the blockchain are all examples of such developments.Techniques such as AI, ML, and DL (deep learning) fall under this umbrella.When seen through the lens of machine learning, this idea becomes clear.However, the model has to be clear and easy to comprehend so that it can inspire confidence in management and lead to the creation of fraud prevention strategies.It would be much easier to evaluate suspicious transactions if there were models that allowed fraud experts to understand why a certain instance was flagged as such.As a result, it would be far less difficult to investigate any fraudulent financial dealings.Potentially suspicious transaction reviews would be made much easier with this modification.Therefore, we propose a variety of data engineering options for improving an analytical model's performance while keeping its interpretability intact.Our data engineering process consists of many phases, each of which is dedicated to a different aspect of feature and instance engineering.
IaaS Cloud Architecture Distributed Cloud Infrastructures and Virtualized Data centers

In data centers, expensive, rigid mainframe-based systems have replaced flexible distributed architectures built on commodity technology.These architectures may be dynamically divided, molded, and modified to accommodate many business procedures and shifting service requirements.Virtualization is crucial in facilitating data center deployment as it separates the system and network from the fundamental physical components and storage management systems.By leveraging virtualized infrastructures, organizations can consolidate servers and provision resources on-demand, leading to heightened rates of server use and significant expenses as well as energy savings.To ensure optimal data center operations, efficiency, and security, it is crucial to control these virtual networks in a maintainable manner.The cloud network manager is crucial at this point in the data center architecture.Delivering infrastructure as a service, which converts conventional data centers into cloud-like designs, is its primary duty.Data centers of the future will resemble personal Internet as a Service platform in terms of being able to execute virtualized services in a flexible and agile manner.The virtual infrastructure manager coordinates the digital deployment of resources, sometimes referred to as the cloud operating system in this context, which also oversees the monitoring and management network for natural and virtual services.By managing the data center as a cloud, it is also feasible to add distant services from other federated data centers or industrial clouds to the local infrastructure.
Medical Image Segmentation Using Deep Learning

Abstract Image segmentation plays an essential role in medical image analysis as it provides automated delineation of specific anatomical structures of interest and further enables many downstream tasks such as shape analysis and volume measurement. In particular, the rapid development of deep learning techniques in recent years has had a substantial impact in boosting the performance of segmentation algorithms by efficiently leveraging large amounts of labeled data to optimize complex models (supervised learning). However, the difficulty of obtaining manual labels for training can be a major obstacle for the implementation of learning-based methods for medical images. To address this problem, researchers have investigated many semi-supervised and unsupervised learning techniques to relax the labeling requirements. In this chapter, we present the basic ideas for deep learning-based segmentation as well as some current state-of-the-art approaches, organized by supervision type. Our goal is to provide the reader with some possible solutions for model selection, training strategies, and data manipulation given a specific segmentation task and dataset.
"AIR HEATING CONTROL SYSTEM BASED ON MICROCONTROLLER "

"This article discusses a technological solution developed on the basis of an Arduino-type microcontroller with automatic control of air heating in production. Currently, microcontrollers have a large range (more than 10,000), which differ among themselves in terms of application, parameters and other properties. Dozens of manufacturers are engaged in the release of microcontrollers. Increasing reliability, speed and functional saturation, as well as reducing the amount of modern computer technology and electricity consumed have led to its widespread use to control complex technological objects with a large number of physical quantities and controlled installations. The result of this article will be the development of a block diagram, drawing up a schematic diagram of a control system, block diagrams, writing a program for a microcontroller, developing an operation algorithm and a set of elements that can be used in any production, production process (for example, drying systems, heating installations, etc.), where an air heating control system is deemed necessary. Also, the main conclusions, concepts of this research work can be used in production processes, household appliances and as a teaching aid. "
Social Audio: Conceptualizing Voice-Based Online Social Networks and their Privacy Implications

This paper explores the nature and implications of social audio: online social networks (OSNs) that enable users to interact via voice. The paper contributes to basic science by offering a precise conceptualization of voice-based OSNs and their design features. We posit that the defining characteristics of traditional OSNs also hold for social audio, yet that novel features (i.e., creating rooms) and modifications of traditional features (i.e., like) through voice idiosyncrasies can be found. This work also shows how social audio introduces novel privacy implications, particularly driven by the richness and risks of voice as an interaction modality. Using three illustrative cases, we demonstrate applications of social audio and how privacy implications remain largely unaddressed. Specifically, we find that the networks considered show very few specific features addressing the risks of voice-based interaction and that current privacy policies do not reflect these risks nor offer mitigation measures. We bridge our findings and extensions for future research by discussing potential approaches in terms of network architecture features for social audio providers to pursue in order to ensure user privacy.
Critique of Non-fungible Token (NFT): Innovation, Analysis and Security Issues

The market for non-fungible tokens (NFT) has been brewing for some time, but NFT is still a minefield of unknowns because we have yet to fully recognize its infinite potential. The notion for NFT stemmed from that of an Ethereum token benchmark which sought to distinguish the difference of one token from all the others. This instance of a token could be used to connect with digital properties as unique exotic identifiers. Now, it can be unanimously traded and it will stimulate the prosperity of decentralized currency market. NFT sales increased by more than 200 times to $17.7 billion in 2021, up from $82.5 million in 2020. Total NFT profits from auctioning or purchasing drastically increased from USD 12 mil throughout 2020 to USD 5.4 billion throughout 2021. The NFT entire ecosystem, on the other hand, is still very much in relative infancy, and so are NFT innovations. Research of state-of-the-art NFT standards, protocols and security evaluations will be carried out.
Quantum walks in the normalized Laplacian

AbstractWe provide a characterization of perfect state transfer in a quantum walk whose Hamiltonian is given by the normalized Laplacian. We discuss a connection between classical random walks and quantum walks only present in this model, and we also rule out several trees as candidates to host perfect state transfer.Keywords: Normalized Laplacianquantum walksstate transferMathematical subject classifications: 81P6805C50 Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingPedro Baptista acknowledges the scholarship UFMG-ADRC (Universidade Federal de Minas Gerais) and the Master's scholarship from CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior). Gabriel Coutinho acknowledges the support of a FAPEMIG (Fundação de Amparo à Pesquisa do Estado de Minas Gerais) grant and the support of CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico).
Logical Forms: Validity and Variety of Formalizations

Formalizations in first-order logic are standardly used to represent logical forms of sentences and to show the validity of ordinary-language arguments. Since every sentence admits of a variety of formalizations, a challenge arises: why should one valid formalization suffice to show validity even if there are other, invalid, formalizations? This paper suggests an explanation with reference to criteria of adequacy which ensure that formalizations are related in a hierarchy of more or less specific formalizations. This proposal is then compared with stronger criteria and assumptions, especially the idea that sentences essentially have just one logical form
Lottery tickets in evolutionary optimization: On sparse backpropagation-free trainability

Lottery tickets in Deep Learning [2] refer to highly sparse neural network initializations, which train to the performance level of their dense counterparts The existence of such sparse trainable initializations has previously been documented for a variety of gradient-based training settings. But is the lottery ticket phenomenon an idiosyncrasy of stochastic gradient descent or does it generalize to evolutionary optimization? In this paper we establish the existence of highly sparse trainable initializations for evolution strategies (ES) and characterize qualitative differences compared to gradient descent (GD)-based sparse training. We introduce a novel signal-to-noise (SNR) iterative pruning procedure, which extracts evolvable sub-networks and incorporates loss curvature information into the network pruning step. We demonstrate the existence of highly sparse evolvable initializations for a wide range of network architectures, evolution strategies and task settings. Furthermore, we find that these initializations encode an inductive bias, which transfers across different evolution strategies, related tasks and even GD-based training. Finally, we compare the local optima resulting from the different optimization paradigms and sparsity levels. In contrast to GD, ES explore diverse and flat local optima and do not preserve linear mode connectivity across sparsity levels and independent runs. The full paper was accepted at the ICML conference [4].