Reviewerly/exHarmony huggingface dataset:
Nonlinear robust estimation for tracking of ballistic target using risk sensitive cubature quadrature Kalman filter

Abstract In this paper, a risk sensitive estimator based on cubature quadrature Kalman filter is formulated and applied for tracking a ballistic object during its re-entry phase. The formulated algorithm is named here as risk sensitive cubature quadrature Kalman filter. The process model and measurement model for two dimensional ballistic target motion is described. The expression for measurement noise covariance is derived for Cartesian coordinate from polar coordinate. Also expression for initial error covariance is derived. The performance of the risk sensitive cubature quadrature Kalman filter is compared with the cubature quadrature Kalman Filter in terms of root mean square error. The simulation results reveal that for wrongly modeled process noise parameter, the risk sensitive filter performs better than their risk neutral counterpart. Moreover, risk sensitive cubature quadrature Kalman filter performs better than risk sensitive cubature Kalman filter. Since expression for measurement noise covariance as well as initial error covariance for two dimensional ballistic target motion is derived, other Gaussian filters may also be applied for tracking of ballistic objects. Article Highlights The process model for two dimensional ballistic target motion is described comprehensively. The expression for measurement noise covariance matrix for linear measurement model is derived for Cartesian coordinate from polar coordinate. The expression for initial error covariance matrix for the filter initialized from the first two measurements is derived. Risk sensitive filter based on cubature quadrature Kalman filter is applied for tracking of two dimensional ballistic target motion and improvement in performance over its risk neutral counterpart is found. Improvement in performance of risk sensitive filter based on cubature quadrature Kalman filter over risk sensitive filter based on cubature Kalman filter is found.
HandyGaze: A Gaze Tracking Technique for Room-Scale Environments using a Single Smartphone

We propose HandyGaze, a 6-DoF gaze tracking technique for room-scale environments that can be carried out by simply holding a smartphone naturally without installing any sensors or markers in the environment. Our technique simultaneously employs the smartphone’s front and rear cameras: The front camera estimates the user’s gaze vector relative to the smartphone, while the rear camera (and depth sensor, if available) performs self-localization by reconstructing a pre-obtained 3D map of the environment. To achieve this, we implemented a prototype that works on iOS smartphones by running an ARKit-based algorithm for estimating the user’s 6-DoF head orientation. We additionally implemented a novel calibration method that offsets the user-specific deviation between the head and gaze orientations. We then conducted a user study (N=10) that measured our technique’s positional accuracy to the gaze target under four conditions, based on combinations of use with and without a depth sensor and calibration. The results show that our calibration method was able to reduce the mean absolute error of the gaze point by 27%, with an error of 0.53 m when using the depth sensor. We also report the target size required to avoid erroneous inputs. Finally, we suggest possible applications such as a gaze-based guidance application for museums.
Calculation Model and Case Study of Water-Saving Quantity in Typical Industrial Enterprises

Water-saving quantity is an important index to measure the effect of water saving. Scientific calculation of water-saving quantity is a basic content of water saving work, and is also an important means to carry out water saving evaluation and guide industrial enterprises to use water reasonably. This paper puts forward that the connotation of water saving is the improvement of water efficiency. Based on this, the calculation model of water saving in typical industrial enterprises is constructed. The water-saving quantity of textile enterprises and beverage production enterprises before and after water-saving technological transformation is calculated, which provides a reference for the calculation of water saving in typical industrial enterprises.
THE FUTURE OF BIG DATA ANALYTICS AND ITS PROGRESS

Big data streams are now widespread because real-time applications that generate enormous amounts of data quickly have proliferated during the past 10 years. Big data is a treasure trove of vast quantities of data. Big data is commonly used in conjunction with other current buzzwords like data science, artificial intelligence, deep learning, and machine learning. Big data has a very promising future because of the demand for data across many technical industries. A massive amount of data has been produced as a result of the COVID-19 global epidemic. Big data analytics is helpful for evaluating, maintaining, and archiving information about COVID-19 patients for later use during the pandemic phase. Big Data Analytics (BDA), a growingly important technology, can significantly enhance sustainable tourism in terms of destination selection, traveller satisfaction, and the quality of travel experiences. Streaming Big Data is the term used to describe data that needs to be saved, processed, and analysed and that is continuously generated from various data sources, including Internet of Things (IoT) devices. The article provides information on - Big data analytics techniques in social media, COVID-19 Pandemic in the New Era of Big Data Analytics, Big data analytics and sustainable tourism, Predictive big data analytics for supply chain demand forecasting, Streaming Big Data Analytics, Big data stream analysis and Big Data Analytics in Crypto Currency. Keywords: Big Data, Streaming Big Data, Predictive Big Data, Sustainable Tourism
Learning Functional Distributional Semantics with Visual Data

Functional Distributional Semantics is a recently proposed framework for learning distributional semantics that provides linguistic interpretability. It models the meaning of a word as a binary classifier rather than a numerical vector. In this work, we propose a method to train a Functional Distributional Semantics model with grounded visual data. We train it on the Visual Genome dataset, which is closer to the kind of data encountered in human language acquisition than a large text corpus. On four external evaluation datasets, our model outperforms previous work on learning semantics from Visual Genome
Efficient Implementation of SPEEDY Block Cipher on Cortex-M3 and RISC-V Microcontrollers

The SPEEDY block cipher family announced at the CHES 2021 shows excellent performance on hardware architectures. Due to the nature of the hardware-friendly design of SPEEDY, the algorithm has low performance for software implementations. In particular, 6-bit S-box and bit permutation operations of SPEEDY are inefficient in software implementations, where it performs word-wise computations. We implemented the SPEEDY block cipher on a 32-bit microcontroller for the first time by applying the bit-slicing techniques. The optimized encryption performance results on ARM Cortex-M3 for SPEEDY-5-192, SPEEDY-6-192, and SPEEDY-7-192 are 65.7, 75.25, and 85.16 clock cycles per byte (i.e., cpb), respectively. It showed better performance than AES-128 constant-time implementation and GIFT constant-time implementation in the same platform. In RISC-V, the performance showed 81.9, 95.5, and 109.2 clock cycles per byte, which outperformed the previous works. Finally, we conclude that SPEEDY can show efficient software implementation on low-end embedded environments.
Preprocessing of Spectroscopic Data Using Affine Transformations to Improve Pattern-Recognition Analysis: An Application to Prehistoric Lithic Tools

The analysis of spectral reflectance data is an important tool for obtaining relevant information about the mineral composition of objects and has been used for research in chemistry, geology, biology, archaeology, pharmacy, medicine, anthropology, and other disciplines. In archaeology, the use of spectroscopic data allows us to characterize and classify artifacts and ecofacts, to analyze patterns, and to study the exchange of materials, etc., as well as to explain some properties, such as color or post-depositional processes. The spectroscopic data are of the so-called “big data” type and must be analyzed using multivariate statistical techniques, usually principal component analysis and cluster analysis. Although there are different transformations of the raw data, in this paper, we propose preprocessing by means of an affine transformation. From a mathematical point of view, this process modifies the values of reflectance for each spectral signature scaling them into a [0, 1] interval using minimum and maximum values of reflectance, thus highlighting the features of spectral curves. This method optimizes the characteristics of amplitude and shape, reduces the influence of noise, and improves results by highlighting relevant features as peaks and valleys that may remain hidden using the raw data. This methodology has been applied to a case study of prehistoric chert (flint) artifacts retrieved in archaeological excavations in the Andévalo area located in the Archaeological Museum of Huelva (Huelva, Andalusia). The use of transformed data considerably improves the results obtained with raw data, highlighting the peaks, valleys, and the shape of spectral signatures.
Three-Dimensional Face Recognition Using Solid Harmonic Wavelet Scattering and Homotopy Dictionary Learning

Data representation has been one of the core topics in 3D graphics and pattern recognition in high-dimensional data. Although the high-resolution geometrical information of a physical object can be well preserved in the form of metrical data, e.g., point clouds/triangular meshes, from a regular data (e.g., image/audio) processing perspective, they also bring excessive noise in the course of feature abstraction and regression. For 3D face recognition, preceding attempts focus on treating the scan samples as signals laying on an underlying discrete surface (mesh) or morphable (statistic) models and by embedding auxiliary information, e.g., texture onto the regularized local planar structure to obtain a superior expressive performance to registration-based methods, but environmental variations such as posture/illumination will dissatisfy the integrity or uniform sampling condition, which holistic models generally rely on. In this paper, a geometric deep learning framework for face recognition is proposed, which merely requires the consumption of raw spatial coordinates. The non-uniformity and non-grid geometric transformations in the course of point cloud face scanning are mitigated by modeling each identity as a stochastic process. Individual face scans are considered realizations, yielding underlying inherent distributions under the appropriate assumption of ergodicity. To accomplish 3D facial recognition, we propose a windowed solid harmonic scattering transform on point cloud face scans to extract the invariant coefficients so that unrelated variations can be encoded into certain components of the scattering domain. With these constructions, a sparse learning network as the semi-supervised classification backbone network can work on reducing intraclass variability. Our framework obtained superior performance to current competing methods; without excluding any fragmentary or severely deformed samples, the rank-1 recognition rate (RR1) achieved was 99.84% on the Face Recognition Grand Challenge (FRGC) v2.0 dataset and 99.90% on the Bosphorus dataset.
Improving Library Service Delivery by the Instrumentality of Knowledge Management Practices: A Critical Review

Purpose: The paper critically examines the nexus between Knowledge Management Practices (KMP) and service delivery in libraries through a detailed review of relevant literature. Special attention was placed on university libraries given their relatively robust system and structures for KMP. The paper also explores how the Socialization, Externalization, Combination and Internalization (SECI) model of Knowledge Management (KM) serves as a tool for the improvement of service delivery in libraries. Method: The approach employed in this study is the critical review of literature. Result: The effective management of knowledge reflected in its creation, organization, sharing and use significantly influences service delivery in libraries. Also, the paper revealed that the SECI model serves as an effective guide in adopting KMP for the improvement of service delivery in libraries. Conclusion: KMP is an influencing factor in the improvement of service delivery in libraries regardless of their type and size.
The Best Efficient Solutions for Multi-Criteria Travelling Salesman Problem Using Local Search Methods

In this research, we propose to use two local search methods (LSM's); Particle Swarm Optimization (PSO) and the Bees Algorithm (BA) to solve Multi-Criteria Travelling Salesman Problem (MCTSP) to obtain the best efficient solutions. The generating process of the population of the proposed LSM's may be randomly obtained or by adding some initial solutions obtained from some efficient heuristic methods. The obtained solutions of the PSO and BA are compared with the solutions of the exact methods (complete enumeration and branch and bound methods) and some heuristic methods. The results proved the efficiency of PSO and BA methods for a large number of nodes ( ). The proposed LSM's give the best efficient solutions for the MCTSP for jobs in a reasonable time.
The Station: A Model for Content Co-Design with Rural Youth

We describe the design, modification, implementation, and preliminary research of a place-based serious AR (augmented reality) application in a rural school setting.The Station is a collaboratively designed iPhone app that allows players to explore the world around them through educational quests.It links the virtual gameplay to real-world locations and discovery via GPS.The quest content is user generated through a companion editor, a web-based platform that allows anyone to design and author their own quest.When used in a student context, the editor allows student users to move from passive consumers of game content to active creators.We follow a group of ninth-grade students from rural western Maine who worked to create content for The Station in collaboration with Maine Mathematics and Science Alliance, who facilitated and researched the project.Youth participants were able to create game content for a real-world location that combined historical, geographical, and scientific knowledge while also learning the importance of storytelling.During our observations of the pilot deployment of this program, key elements emerged that should be incorporated as part of the codesign process for users to successfully create content for The Station.Our model can be useful in engaging youth as placebased content creators in both informal and formal education settings.
Unconventional Narrative Structure and The Problem of Latency Phase in Peter Pan Novel

PurposeThis research aimed to explain the human’s latency phase through child characters in Peter Pan novel and its relation to the novel unconventional narrative structure. MethodThe research was qualitative using a descriptive case design. The novel’s intrinsic elements were analyzed to see the complexity of this children's literature that led to the analysis of the latency phase of the child characters through the make-believe play. The analysis made uses of Sigmund Freud’s concept of psychosexual personality development. Results/FindingsThis research found that the novel’s complexity came from its unconventional narrative structure such as the layered sub-plots, unique settings, and characters with complex personalities that were uncommon in children’s literature. It then led to the further analysis of the children who were processing their latency phase through their activities. Focusing on Peter as a captain and a father and Wendy as a mother, all the roles manifested their wishes and desires. ConclusionThe research concluded that the unique, inconsistent, and illogical points from the unconventional narrative structure could be explained through the analysis of the story’s issue about the latency phase, which shows complexity found despite its status as children’s literature.
Optical Character Recognition Using Tesseract

Abstract: Optical Character Recognition (OCR) is a process or technology in which text within a digital image is recognized. With rapid pace of technology, people want quicker, handy and reliable tools, which can fulfil their daily needs. With this moto we had gone forward and analyzed the existing tools and made up this Android App, which provides seamless experience (No ads and easy-to-use), and great accuracy. The main objective of this project is to allow automatic extraction of the information that a user wants from the paper document and using it wherever it is needed. In this project, OCR uses Tesseract as an engine to display the text to the user and uses a Deep learning model to classify the letters and display them to the user. It adds a new neural network (LSTM) based OCR engine which is focused on line recognition but also still supports the legacy Tesseract OCR engine which works by recognizing character patterns.
Dat als

Dutch style guides and teaching methods consider the direct sequence of the subordinating conjunctions dat ‘that’ and als ‘if’ a stylistic lapse. It is advised to rewrite sentences containing the sequence by repositioning the conditional als-clause to the final position of the sentence. This study aims to find out to what extent this stylistic lapse actually occurs in several types of texts, and in which situations the aforementioned advice is suitable. We use corpus data from different modes and registers, we provide an overview of language advice from grammars, style guides and teaching methods, and we provide a comparison of Dutch dat als to the treatment of that if in English usage guides. Finally, we analyze the effects of the proposed reformulations, focusing on the function of the conditional clause in context. By doing so, we aim to formulate an informed view on the wide-spread advice concerning dat als ‘that if’, and we aim to refine the advice available in the literature.
On the Strategies of Machine Translation Post-Editing Based on Cloud Platform

In the era of AI, machine translation post-editing(MTPE) based on cloud platform is an important part in the translator's translation environment. This study analyzes the existing problems in machine translation, and puts forward specific post-editing strategies from the aspects of lexis, syntax, text, style and format, in order to provide some references to improve the quality of post-editing based on computer-aided translation software.
Training Model for Professional Language Service Competence: A Case Study with Sports English Service Talents

Language service competence is central to language service and is the basis of professional language service talents training. Although the demand for professional language service talents increases dramatically in various fields in China, studies on professional language service competence have been quite limited, thus affecting the training and supply of professional language service talents. In view of this, this study first discusses the definition and componential contents of professional language service competence, and on that basis, takes Sports English service talents as an example to design a training model for professional language service competence. This training model, combining on-campus education and off-campus practice, can promote the development of professional language service competence in an efficient and holistic way. The training effect shows that the training model works well and the students who have received training can meet the demands of various language services in sports field. It is hoped that this study can provide some enlightenment for further research and training of professional language service competence.
The Relationship between intrinsic motivation of elementary school students and class satisfaction in remote physical education classes during the COVID-19 situation: The moderating effect of teacher’s feedback

목적: 본 연구는 코로나19 상황에서 실시된 원격체육수업을 경험한 초등학생들을 대상으로 학생들의 내적동기와 체육수업 만족도의 관계에서 학생들이 지각하는 교사 피드백의 조절효과(상호작용효과)를 분석하여 원격체육수업이라는 특수한 환경에서 수업의 효과성 제고에 필요한 시사점을 도출하고자 하였다.방법: 173명의 초등학교 5, 6학년 학생들을 대상으로 온라인설문조사를 실시하여 자료를 수집하였고 AMOS’ 21.0과 SPSS 26.0 통계 프로그램을 사용하여 자료를 분석하였다.결과: 첫째, 체육수업 내적동기와 교사피드백은 각각 원격체육수업만족도와 정적 상관관계가 나타났다. 둘째, 체육수업 내적동기와 교사피드백은 원격체육수업만족도에 긍정적인 영향을 미치는 것으로 나타났으며, 체육수업 내적동기만을 독립변인으로 투입한 모형에 비해 교사피드백을 추가로 투입한 모형의 설명량이 유의하게 증가한 것으로 나타났다. 셋째, 내적동기와 교사피드백의 상호작용 항을 투입한 모형의 설명량이 유의하게 증가함으로써 원격체육수업만족도에 미치는 교사피드백의 조절효과를 검증하였다.결론: 본 연구를 통해 교사의 적극적인 피드백이 원격수업에서 학생들에게 다가가기 위한 효과적인 교육방법이라는 것을 확인하였으며, 원격체육수업만족도를 높이기 위한 교수 및 학습 방법에 대한 논의의 필요성을 제안하였다. Purpose: This study investigated physical education(PE) class satisfaction for elementary school students who experienced remote PE classes conducted in the COVID-19 situation, and verified how the intrinsic motivation(IM) and the level of teacher’s feedback(TF) perceived by students affected remote PE class satisfaction through quantitative research methods. In addition, by analyzing the moderating effect (interaction effect) of TF perceived by students in the relationship between students" IM and PE class satisfaction, it was intended to derive implications for enhancing the effectiveness of classes in a special environment called remote PE class.Methods: An online survey was conducted on 173 fifth and sixth graders and data were collected, and data were analyzed using AMOS 21.0 and SPSS 26.0 statistical programs.Results: First, IM for PE classes and TF showed a positive correlation with remote PE class satisfaction, respectively. Second, it was found that IM for PE and TF had a positive effect on remote PE class satisfaction, and the explanation amount of the model that additionally inserted TF was significantly increased compared to the model that only IM was inserted as an independent variable. Third, the moderating effect of TF on remote PE class satisfaction was verified by significantly increasing the amount of explanation of the model that inserted the interaction term between IM and TF.Conclusion: It was confirmed that active feedback from teachers was an effective educational method to satisfy students in remote classes, and the necessity of discussing teaching and learning methods to increase satisfaction with remote PE classes was suggested.