Reviewerly/exHarmony huggingface dataset:
A New Linear Regression Kalman Filter with Symmetric Samples

Nonlinear filtering is of great significance in industries. In this work, we develop a new linear regression Kalman filter for discrete nonlinear filtering problems. Under the framework of linear regression Kalman filter, the key step is minimizing the Kullback–Leibler divergence between standard normal distribution and its Dirac mixture approximation formed by symmetric samples so that we can obtain a set of samples which can capture the information of reference density. The samples representing the conditional densities evolve in a deterministic way, and therefore we need less samples compared with particle filter, as there is less variance in our method. The numerical results show that the new algorithm is more efficient compared with the widely used extended Kalman filter, unscented Kalman filter and particle filter.
Growth curve mixture models with unknown covariance structures

Though playing an important role in longitudinal data analysis, the uses of growth curve models are constrained by the crucial assumption that the grouping design matrix is known. In this paper we propose a Gaussian mixture model within the framework of growth curve models which handles the problem caused by the unknown grouping matrix. This allows for a greater degree of flexibility in specifying the model and freeing the response matrix from following a single multivariate normal distribution. The new model is considered under two parsimonious covariance structures together with the unstructured covariance. The maximum likelihood estimation of the proposed model is studied using the ECM algorithm, which clusters growth curve data simultaneously. Data-driving methods are proposed to find various model parameters so as to create an appropriate model for complex growth curve data. Simulation studies are conducted to assess the performance of the proposed methods and real data analysis on gene expression clustering is made, showing that the proposed procedure works well in both, model fitting and growth curve data clustering.
Adaptive Alternating Stochastic Gradient Descent Algorithms for Large-Scale Latent Factor Analysis

Latent factor analysis (LFA) is highly efficient in knowledge discovery from high-dimensional and sparse (HiDS) matrices frequently encountered in big data and web service related applications. A stochastic gradient descent (SGD) algorithm is commonly adopted as a learning algorithm for LFA owing to its high efficiency. However, its sequential nature makes it less scalable when processing large-scale data. Although an alternating SGD algorithm decouples an LFA process to achieve parallelization, its performance relies on its hyper-parameter selection that is highly expensive to tune. To address it, this paper presents three adaptive alternating SGD algorithms, thus leading to three Parallel Adaptive LFA (PAL) models for LFA on large-scale HiDS matrices. Experimental studies on HiDS matrices from industrial service applications show that the proposed PAL models perform significantly better than existing ones in terms of both convergence rate and computational efficiency, as well as achieve competitive prediction accuracy for missing data.
UAV RF Fingerprinting with Power Spectra Estimates

There is a vital need to detect and classify different radio-controlled Unmanned Aerial Vehicles (UAVs), and that need will only grow as they gain wider use. Using Radio Frequency (RF) fingerprinting is a stealthy way to classify drone types and their operating modes without making any RF transmissions. In this work, we perform several tests to advance the current literature in detecting and classifying UAVs. We find that image classifiers can perform similarly to and even outperform 1D coefficient-based classifiers. We also demonstrate the effect of different Discrete Fourier Transform (DFT) averaging, a common technique used to reduce the noise variance of power spectra estimates. We found that increasing the averaging before creating our images provides increased accuracy and reduced training time.
Mask Detection in Crowded Environment using Machine Learning

COVID-19 epidemic has affected many people across the world creating a health crisis around the globe. World Health Organization (WHO) recommends that covering face with mask is one of the best protection to minimize COVID infection. COVID-19 pandemic has forced governments across the planet to impose strict lockdowns to stop the spread of virus. Many health experts says that wearing facemasks while at public places clearly reduces the spread of the virus. A Machine Learning (ML) based face mask detection with face recognition and alert has been proposed. As part of this work, a face mask detector along with face recognition with CV and ML using Python has been developed. In this system, the input would be a real time input image via laptop webcam. Assuming if anyone is not wearing a mask, an automatic alert is generated and the respective person snapshot is sent to the admin e-mail.
The development strategy of Trigona sp beekeeping at SMKN 4 Luwu

Abstract Forest has various sources that can be utilized and honey is one of the non-timber forest products produced from Trigona sp beekeeping at SMKN 4 Luwu. This research is intended to describe the Trigona sp beekeeping business and identify the internal and external factors as the strength, weaknesses, opportunities and challenges from beekeeping business and so it can be formulated a good strategy to be carried out with using SWOT matrix. The analysis result shown the Strength, Weakness, Opportunity Threats. The alternative strategy that can be applied to develop the beekeeping business based on the analysis result of SWOT which dealing with the government for capital support, deligating the members to participate in beekeeping training, the availability of sustainable feed sources, utilizing the modern technology, using the compelling packaging, implementing the SOP in the packaging process so that the packaging is more hygienic, increasing the production volume, selling with competitive price, marketing the products through technology and keeping the school environment safety by installing the CCTV in the area of beekeeping business.
Application of Blockchain Technology in Supply Alliance Chain Under Big Data Time Domain

Abstract The 21st century is the era of big data. Data is growing at a massive amount of time, and the value of data itself is increasing rapidly. In the era of big data, the demand for security continues to increase, and traditional methods are obviously unable to meet the increasing demand. As a trust-building machine, blockchain technology, especially the multi-center architecture and multi-institutional cooperation scenarios of the alliance chain, satisfies the needs of cross-domain authentication among regions, industries, and enterprises. The purpose of this article is to analyze and discuss the application of blockchain technology in the supply alliance chain under the time domain of big data. This article first introduces the core technology of blockchain through an overview of blockchain theory, and analyzes the status quo of supply alliance chains. Finally, it integrates big data and blockchain technology to explore the supply of blockchain technology in the time domain of big data. Experimental research shows that compared with the traditional supply alliance chain technology, the supply alliance chain that applies the blockchain technology in the big data time domain is more feasible.
Comparative Analysis of Transform Domain Watermarking System Based on Performance Measures

In last few decades, data security has been a challenging issue to protect the copyright information and integrity of digital products. So digital watermarking is founded as one of the talented methods to defend digitized media. This paper defines functioning of major transform domain methods such as discrete cosine transform (DCT), quaternion Hadamard transform (QHT), discrete wavelet transform (DWT), discrete contourlet transform (DCT), and many more are discussed. Comparison of these techniques based on peak signal-to-noise ratio (PSNR) and normalized correlation (NC) is discussed in this paper. Various types of attacks are also evaluated on these techniques.
Recognition of Indian Sign Languages

The focal point of this project is about developing a user interface to aid people who have hearing and speaking disabilities. This application would help in recognizing the different signs with respect to the single user. The real-time image is captured using a webcam and dynamically stored. The elements are initialized and saved in KN Neighbor classifiers. These classifiers are used to load the KN Neighbor model. This model predicts the signs dynamically and is built using machine learning techniques. Mobile Net is used as a machine learning package. This model was implemented in three phases. The first phase deals with the user interface where user images are captured using a webcam. In the second phase, the initialized elements are used by the KN Neighbor classifier and then stored as the KN Neighbor model. The third phase involves the prediction of signs.
Document Representation with Representative Sets and Document Similarity at Sentence Level Using Maximum Matching in Bipartite Graph

It would be more convenient for users if we can group various news articles from different sources, such as online newspapers or social media channels, based on content similarity. Furthermore, it is also necessary to visually support users to find the semantic similarity between two documents, or between a document and various topics. Therefore, in this paper, we propose a method for the representation of a news article, or text document, in general, with a single or multiple representative sets, each of which consists of sentences with similar semantics. We propose to use the disjoint-set approach to find the representative set for a text document. Then we propose a method to evaluate the similarity between two news articles at sentence level using the maximum matching in a bipartite graph, solved with the maximum flow and minimum cost. In this way, we can evaluate the similarity between two documents at a finer granularity, i.e. sentence level. We also apply our approach to evaluate the relationship of each sentence in a document with various topics. Evaluation on our dataset with 37,221 Vietnamese news articles on 18 topics from 5 online newspapers demonstrates the promising applicability of our proposed methods. Our methods can achieve more than 90% of accuracy for sentence similarity matching in Vietnamese news articles, nearly 70% of accuracy for verifying two articles in the same category or not, and the \(recall@K = 83.8\%\) with \(K=3\) for main topic classification. We also develop online tools for visualizing document similarity at the sentence level, finding main sentences in a document, and the relationship between sentences in a document with different topics.
Інтерактивна дошка як засіб організації уроку інформатики за типом «Ротація станцій»

The article reveals the directions of using the interactive whiteboard in the educational process. The description of standard technical and didactic capabilities of the tool is given. New approaches to organizing and conductinginformatics lessons by the type of «Station Rotation» using the interactive whiteboard are described.
Fault Detection for ZPW-2000A Jointless Track Circuit Based on Deep Belief Network Optimized by Grey Wolf Optimizer Algorithm

Abstract At present, the fault detection for jointless track circuit still depends on the experience of electrical personnel, which leads to low maintenance efficiency and the phenomenon of misjudgment and missed judgment. A fault detection method for track circuit based on deep belief network (DBN) is proposed in this paper. According to the working principle and transmission characteristics of track circuit, 12 voltage and current monitoring parameters are selected to detect 15 types of track circuit fault. However, the selection of structural parameters for deep belief network is time-consuming, so grey wolf optimizer algorithm (GWO) is proposed to optimize DBN, which can adaptively select the number of neurons in each hidden layer. The simulation results show that by introducing the GWO algorithm to optimize the DBN detection model, the fault classification accuracy of the ZPW-2000A jointless track circuit can reach 96.96%, which significantly improves the fault detection level of the track circuit.
Entropy Modeling of Random Vectors in the Presence of Discrete Components

Entropy modeling is widely used in the study of open stochastic systems in various fields. However, when using differential entropy to model stochastic systems, all components of a random vector must be continuous random variables. In practice, the phenomena under study are usually continuous, and discreteness occurs when data is grouped or when moving to scoring indicators. The article describes the technique of entropy modeling of multidimensional stochastic systems on the example of groupings and score indicators. It is shown that differential entropy can be used in modeling discrete random variables for cases when they are obtained as a result of grouping data or when moving to scoring indicators. This is achieved by switching from discrete random variables to their approximations by continuous random variables having piecewise linear distribution functions. Examples are considered.
Super Learner: Stack Generalization Algorithm for AutoML

Model Selection and Ensembling are important and tedious tasks in the process of machine learning. To implement these processes, a lot of knowledge about machine learning as well as the application domain is needed. Often, many researchers perform trial and error methods to find the optimum model architecture for the application, and this process may take an indefinite amount of time. AutoML reduces the burden on the user and saves time, which can be used to invest in other important tasks like data gathering and analyzing the results produced. There are various state-of-the-art ensembling techniques like bagging, boosting, stacking, blending, etc. In this paper, we propose a variant of Super Learner which is a finely-tuned algorithm based on Stack Generalization. This algorithm is proposed for supervised learning on tabular-labeled datasets for prediction and classification tasks. We compare the performance of this ensemble with the traditional super learner algorithm as well as individual base models. These results show that the proposed variant of Super Learner outperforms the base learner models and the traditional Super Learner algorithm.
Eye-Tracking Assistive Technology for Hands-Free Computer Navigation

Eye-tracking technologies for computer navigation assist those with mobility and verbal impairments in interacting with the world around them. Studies found that these technologies have a profound positive impact on the lives of these individuals and those who care for them. This proceeding explores the popular features of eye-tracking systems and the challenges they present. Specifically, this proceeding evaluates camera configurations, camera types, and command selections through the lenses of cost, ease of use, and comfort. Current and future design guidelines based on these criteria are recommended to best aid individuals with these impairments.
Reliably estimating the statistical significance of a new physics signal by exploiting GPUs

GPUs represent one of the most sophisticated and versatile parallel computing architectures that have recently been introduced in the High Energy Physics (HEP) field.GooFit is an open source tool interfacing ROOT/RooFit to the CUDA platform that allows to manipulate probability density functions and perform fitting tasks.The striking performances and computing capabilities of GPUs, in comparison to traditional CPU cores, have been exploited in the application of a highstatistics pseudo-experiment method implemented in GooFit, with the purpose of estimating the local or global statistical significance of a physics signal, already known or new respectively.When dealing with an unexpected new signal, a global significance must be estimated to take into account the Look-Elsewhere-Effect and this is accomplished coupling a clustering-based scanning technique to the pseudo-experiments method, also without introducing any relevant systematic uncertainty.By means of these tools it has been possible to investigate the approximation characterizing modern, and currently widely used, statistical methods.In particular two studies have been carried out: 1) the asymptotic behaviour of a likelihood ratio test statistics (Cowan-Cranmer-Gross-Vitells) has been investigated while estimating the local statistical significance of a known signal, 2) the approximation of the Gross-Vitells method (trial factors) has been explored while estimating the global statistical significance of a new signal.These studies have been collected and presented here coherently with a didactic approach.Indeed this work is currently used in lectures about Statistics for Data Analysis.However the presented results can be a useful reference for the confirmation -by means of GPUs -of the validity of few asymptotic formulas/methods now commonly used in HEP.
Research on Personalized Learning Path Management Based on Blockchain Technology

Aiming at the problem that learners' personal information, courses and relevant certificates can not provide detailed information about learning depth and learning or teaching methods, this paper takes learners' learning information, learning record smart contract and Ethereum Blockchain related technology as the research object, aiming to use Ethereum Blockchain and smart contract to connect learners' learning records. On the one hand, it can use the block data stored in the Blockchain to easily verify the authenticity of the data and ensure the privacy and security of the data. On the other hand, it can provide personalized learning path for learners and be used to evaluate a person's educational achievement, employment suitability and intelligence evaluation.
Asymmetric Supervised Deep Discrete Hashing Based Image Retrieval

Hashing has been widely used for image retrieval tasks. In view of the limitations of existing deep supervised hashing methods, a new asymmetric supervised deep discrete hashing method is proposed to maintain the semantic structure between different categories and generate binary codes. Firstly, a deep network is used to extract image features and reveal the similarity between each pair of images according to their semantic labels. To enhance the similarity between binary codes and ensure the retention of multi-label semantics, this paper design an asymmetric hashing method that utilizes a multi-label binary code mapping to make the hash codes have multi-label semantic information. In addition, the bit balance of the binary code is introduced to balance each bit, which encourages the number of -1 and +1 to be approximately similar among all training samples. Experimental results on two benchmark datasets show that the proposed method is superior to other methods in image retrieval.
Sonic Robotics: Musical Genres as Platforms for Understanding Robotic Performance as Cultural Events

This chapter examines how artist Wade Marynowsky’s recent robotic performance art projects are framed within musical genres: Opera, in Robot Opera (2015), Ambient/Glitch, in Synthesiser-Robot (2017); and Disco, in The Ghosts of Roller Disco (2020). By positioning the projects within known music genres, the research expands the canon of Cultural RoboticsCultural robotics by providing platforms that allow wider communities to understand the presentation of robotic performance as cultural events within a historical context. Notions of robotic agencyAgency, dramaturgy, choreographyChoreography, robotic musical gesture, and robotic musicianship are explored across three case studies, which are presented in the contexts of live performance festivals and durational exhibitions: (1) Robot Opera, a dramaturgically designed, interactive opera for eight, larger than life-sized robots; (2) Synthesiser-Robot, a solo autonomousAutonomous robot performance for a repurposed industrial robotIndustrial robot arm, theUR3, and a hardware-software interface, the Ableton Push; and (3) The Ghosts of Roller Disco, a choreographed performance for eight robotic roller skates. The research highlights the importance of robotic agencyAgency by applying autonomousAutonomous and interactive movementMovement, localised sound, and surround sound design in creating immersive and engaging robotic performance art experiences.
On algebraic and topological semantics of the modal logic of common knowledge S4CI

We investigate algebraic and topological semantics of the modal logic S4CI and obtain strong completeness of the given system in the case of local semantic consequence relations. In addition, we consider an extension of the logic S4CI with certain infinitary derivations and establish strong completeness results for the obtained system in the case of global semantic consequence relations. Furthermore, we identify the class of completable S4CI-algebras and obtain for them a Stone-type representation theorem.
Density-Assessment for Breast Cancer Diagnosis Using Deep Learning on Mammographic Image: A Brief Study

Among the female population, breast cancer has been the most common fatal disease. Breast density, a risk factor for breast cancer, is a radiologic feature that represents the content of fibro glandular tissue relative to the area or volume of the breast. Breast density is one of the key attributes for breast cancer diagnosis to suggest screening and follow-ups. Although there have been many publications on the mammogram classification for cancer diagnosis, limited reviews have been published which include a comprehensive explanation of breast cancer density assessment using deep learning on mammographic images. We have placed particular focus on the breast density class assessment using Convolutional Neural Network (CNN) with Mammographic Image. The purpose of this work is to provide a brief study of the literature on recent developments in density-based diagnosis of breast cancer using deep learning on multi-view mammographic Image.
Knowledge Discovery in COVID-19 Research Literature

This paper presents the preliminary results of an ongoing project that analyzes the growing body of scientific research published around the COVID-19 pandemic.In this research, a general-purpose semantic model is used to double annotate a batch of 500 sentences that were manually selected from the CORD-19 corpus.Afterwards, a baseline text-mining pipeline is designed and evaluated via a large batch of 100, 959 sentences.We present a qualitative analysis of the most interesting facts automatically extracted and highlight possible future lines of development.The preliminary results show that general-purpose semantic models are a useful tool for discovering fine-grained knowledge in large corpora of scientific documents.Phylogenetic studies have shown that 2019-nCoV and SARS-CoV belong to the subgenus Sarbecovirus, but they are distantly related 5-8 , with a sequence identity of 79.6% at the whole-genome level 5 However, SARS-CoV, using the same receptor, was not detected in skeletal muscle by post-mortem examinationIn conclusion, SARS-Cov is the closest related virus to 2019-nCoV for which a significant number of epitopes has been defined in humans (and other species), and that also causes human disease with lethal outcomes Concept Concept Concept ,, Action Concept Con Concept in-context has-