Reviewerly/exHarmony huggingface dataset:
Maneuvering target state estimation based on separate modeling with B-splines

The state estimation of a maneuvering target based on separate modeling of target trajectory shape and dynamic characteristics is studied in the absence of prior information about the target trajectory. Traditionally, target dynamics are described by motion models in Cartesian coordinates, in which the target trajectory is determined by target dynamic characteristics. However, due to target motions subjected to external environmental limitations such as roads, terrains, buildings, trees, flight routes, and sea-routes, the trajectory shape of a target is independent of target dynamic characteristics. In addition, although one-dimensional representation can be used to decouple the target trajectory from dynamic characteristics, it is unsuitable for direct tracking without knowing prior information about the target trajectory. For these reasons, this paper proposes a new estimation algorithm that models the target trajectory and dynamic characteristics separately as well as handles the issue of no prior information. The target trajectory over a sliding window is described by B-spline curves, which are functions of the arc length. The unknown control points defining the B-spline curves are put in the modified multiple model estimator framework and are estimated together with the base state. After the first estimation cycle, the control points entering into interaction stage of the proposed estimation algorithm are reset by the least squares (LS). Simulation results show that the proposed estimation algorithm is capable of achieving better performance than conventional tracking algorithms.
Application of Optimization Technology for Overhaul Decision of Substation Equipment Based on Machine Learning

Abstract With the continuous improvement of living standards and the continuous increase of electricity load, the number of power transmission and transformation equipment also increases rapidly. The original maintenance mode is not enough to guarantee the safe operation of the huge power grid. This paper mainly studies the research and application of machine learning based maintenance decision optimization technology for substation equipment. Starting from the technical principles of online monitoring and condition maintenance of substation equipment, this paper has realized an intelligent monitoring and maintenance early warning system combined with deep learning model. The main functions of this system include monitoring device management, operation monitoring and comprehensive display, etc., which can effectively carry out online monitoring and state early warning of substation equipment. It greatly improves the intelligent degree of operation and management of substation equipment, saves the cost of traditional manual monitoring, and effectively prevents the economic loss caused by substation equipment failure, which has far-reaching significance for promoting the construction of smart power grid.
Deformed explicitly correlated Gaussians

Deformed correlated Gaussian basis functions are introduced and their matrix elements are calculated. These basis functions can be used to solve problems with nonspherical potentials. One example of such potential is the dipole self-interaction term in the Pauli-Fierz Hamiltonian. Examples are presented showing the accuracy and necessity of deformed Gaussian basis functions to accurately solve light-matter coupled systems in cavity QED.
Designing Ray-Pointing using Real hand and Touch-based in Handheld Augmented Reality for Object Selection

Augmented Reality (AR) have been widely explored worldwide for their potential as a technology that enhances information representation. As technology progresses, smartphones (handheld devices) now have sophisticated processors and cameras for capturing static photographs and video, as well as a variety of sensors for tracking the user's position, orientation, and motion. Hence, this paper would discuss a finger-ray pointing technique in real-time for interaction in handheld AR and comparing the technique with the conventional technique in handheld, touch-screen interaction. The aim of this paper is to explore the ray pointing interaction in handheld AR for 3D object selection. Previous works in handheld AR and also covers Mixed Reality (MR) have been recapped.
Verdict Inference with Claim and Retrieved Elements Using RoBERTa

Automatic fact verification has attracted recent research attention as the increasing dissemination of disinformation on social media platforms.The FEVEROUS shared task introduces a benchmark for fact verification, in which a system is challenged to verify the given claim using the extracted evidential elements from Wikipedia documents.In this paper, we propose our 3 rd place three-stage system consisting of document retrieval, element retrieval, and verdict inference for the FEVER-OUS shared task.By considering the context relevance in the fact extraction and verification task, our system achieves 0.29 FEVER-OUS score on the development set and 0.25 FEVEROUS score on the blind test set, both outperforming the FEVEROUS baseline.
Abstract and Image Analysis of High-Temperature Materials from Scientific Journals Using Deep Learning and Rule-Based Machine Learning Approaches

In this digital world, many research papers have been getting added to the scientific journals in PDF format. It has become essential to the process and needs digitalization due to the exponential growth of newly published research papers. Survey of Abstract, Figures, and Captions convey the necessary information and extracted from scientific research journals. The section algorithm is used for extracting “Abstract” and “Result and Discussion” sections from the published research papers about high-temperature materials (HTM) by converting PDF to word documents. HTM got complete applications in aerospace engineering and electronic. After exposure to high-temperature test facilities, various characterization techniques are used to produce numerous high-resolution images. Abstract summarization, SEM figures, and caption extraction from HTM documents are performed using Deep learning, PDFFigure, and Machine learning (ML) techniques. MatLab is used for feature extraction and to classify SEM images by rule-based ML techniques. This summarization helps new researchers understand existing research datasets and proceed with new approaches to solve more complex problems. This research paper presents automatic extraction and summary of abstract, high-resolution figures, and captions from scientific journals on HTM; all these documents will be analyzed to categorize with a different tailor-made approach using deep learning and machine learning techniques.
Data Collection Smart and Simple: Evaluation and Metanalysis of Call Data From Studies Applying the 5Q Approach

Agricultural development projects often struggle to show impact because they lack agile and cost-effective data collection tools and approaches. Due to the lack of real-time feedback data, they are not responsive to emerging opportunities during project implementation and often miss the needs of beneficiaries. This study evaluates the application of the 5Q approach (5Q). It shows findings from analyzing more than 37,000 call log records from studies among five countries. Results show that response rate and completion status for interactive voice response (IVR) surveys vary between countries, survey types, and survey topics. The complexity of question trees, the number of question blocks in a tree, and the total call duration are relevant parameters to improve response and survey completion rate. One of the main advantages of IVR surveys is low cost and time efficiency. The total cost for operating 1,000 calls of 5 min each in five countries was 1,600 USD. To take full advantage of 5Q, questions and question-logic trees must follow the principle of keeping surveys smart and simple and aligned to the project's theory of change and research questions. Lessons learned from operating the IVR surveys in five countries show that the response rate improves through quality control of the phone contact database, using a larger pool of phone numbers to reach the desired target response rate, and using project communication channels to announce the IVR surveys. Among other things, the respondent's first impression is decisive. Thus, the introduction and the consent request largely determine the response and completion rate.
Drivers Glance Like Lizards during Cell Phone Distraction in Assisted Driving

Head pose has been proposed as a surrogate for eye movement to predict areas of interest (AOIs) where drivers allocate their attention. However, head pose may disassociate with AOIs in glance behavior involving zero or subtle head movements, commonly known as “lizard” glance pattern. In contrast, “owl” glance pattern is used to describe glance behavior along with larger head movements. It remains unclear which glance pattern is prevalent during driver cell phone distraction and what are appropriate metrics to detect such distraction. To address this gap, we analyzed the gaze direction and head pose of 36 participants who completed an email-sorting task using a cell phone while driving a Tesla on the test track in Autopilot mode. The dispersion-threshold algorithm identified driver gaze fixations and synchronized them with head movements. The results showed that when using a cell phone either near the lap or behind the steering wheel, participants exhibited a dominant lizard-type glance pattern with minimal shift in head position. As a result, head pose alone may not provide sufficient information for cell phone distraction detection, and gaze metrics should be involved in enhancing this application.
A Framework for Secure Logging in Precision Healthcare Cloud-based Services

Cloud-based precision health care services will soon be integrated with existing health care systems. Since health care data is sensitive, it is crucial to maintain the integrity of audit logs that allow organizations to track system usage, protect patient privacy, and provide forensic evidence. In this paper, we develop a secure logging framework for precision healthcare services. We implement the framework using blockchain and show that our logging system is tamper-resistant and can ensure integrity.
Climate change and the legitimacy of Bitcoin

This paper examines the ways in which Bitcoin miners are responding to external pressures associated with climate change and what this means for the legitimacy of Bitcoin. Using the experiences of four miners with very different energy use practices, the paper unpacks the various motivations and choices that underpin Bitcoin mining, including factors that can be obscured in economic and data models. Miners make decisions based on cost, local partnerships, the preferences of capital investors, energy market dynamics, technology upgrades, and the regulatory environment. In responding to these pressures and events, some miners are providing services and innovations that may help the viability of clean energy infrastructures for energy providers and beyond, including the data and computing industry. The paper finds that if Bitcoin loses legitimacy as a store of value, then it may result in lost opportunities to accelerate sustainable energy infrastructures and markets.
Improving Students’ Vocabulary Mastery Through the Keyword Technique At The Tenth Grade of SMAN 2 Tulang Bawang Tengah

The aim of this research was to improve students’ vocabulary mastery of the tenth grade through the use of Keyword technique. This research is categorized as classroom action research. The subject of this research was the tenth grade students of SMAN 2 Tulang Bawang Tengah in the academic year of 2021/2022. The data were analyzed by using descriptive statistics and quantitative data analyses. This research was conducted in two cycles where each cycle consisted of three meetings. Based on the analysis of the research findings, the students’ vocabulary mastery was improved by using Keyword technique based on the quantitative and qualitative data. From quantitative data, the result showed that the mean score of each cycle was increased from post-test of cycle 1 to cycle 2. The mean score of cycle 1 was 62.07, there were 13 students or 46.43% who passed Minimum Mastery Criterion, while in cycle 2 was 77.93, there were 21 students or 75% who passed Minimum Mastery Criterion. Moreover, this improvement can also be seen from qualitative data, such as the results of students’ observation sheet from cycle 1 to cycle 2. The mean score of cycle 1 was 6.41, while in cycle 2 was 7.31. The improvement of students’ learning activity from cycle 1 to cycle 2 was 0.90. It showed that the students seemed to be more motivated and confidence in learning vocabulary. Therefore, it can be concluded that the implementation of the Keyword technique can improve students’ learning activities and vocabulary mastery.
Automatic RadLex coding of Chinese structured radiology reports based on text similarity ensemble

Abstract Background Standardized coding of plays an important role in radiology reports’ secondary use such as data analytics, data-driven decision support, and personalized medicine. RadLex, a standard radiological lexicon, can reduce subjective variability and improve clarity in radiology reports. RadLex coding of radiology reports is widely used in many countries, but translation and localization of RadLex in China are far from being established. Although automatic RadLex coding is a common way for non-standard radiology reports, the high-accuracy cross-language RadLex coding is hardly achieved due to the limitation of up-to-date auto-translation and text similarity algorithms and still requires further research. Methods We present an effective approach that combines a hybrid translation and a Multilayer Perceptron weighting text similarity ensemble algorithm for automatic RadLex coding of Chinese structured radiology reports. Firstly, a hybrid way to integrate Google neural machine translation and dictionary translation helps to optimize the translation of Chinese radiology phrases to English. The dictionary is made up of 21,863 Chinese–English radiological term pairs extracted from several free medical dictionaries. Secondly, four typical text similarity algorithms are introduced, which are Levenshtein distance, Jaccard similarity coefficient, Word2vec Continuous bag-of-words model, and WordNet Wup similarity algorithms. Lastly, the Multilayer Perceptron model has been used to synthesize the contextual, lexical, character and syntactical information of four text similarity algorithms to promote precision, in which four similarity scores of two terms are taken as input and the output presents whether the two terms are synonyms. Results The results show the effectiveness of the approach with an F1-score of 90.15%, a precision of 91.78% and a recall of 88.59%. The hybrid translation algorithm has no negative effect on the final coding, F1-score has increased by 21.44% and 8.12% compared with the GNMT algorithm and dictionary translation. Compared with the single similarity, the result of the MLP weighting similarity algorithm is satisfactory that has a 4.48% increase compared with the best single similarity algorithm, WordNet Wup. Conclusions The paper proposed an innovative automatic cross-language RadLex coding approach to solve the standardization of Chinese structured radiology reports, that can be taken as a reference to automatic cross-language coding.
Performance Evaluation of Data-Centric Workloads in Serverless Environments

Serverless computing is a cloud-based execution paradigm that allows provisioning resources on-demand, freeing developers from infrastructure management and operational concerns.It typically involves deploying workloads as stateless functions that take no resources when not in use, and is meant to scale transparently.To make serverless effective, providers impose limits on a per-function level, such as maximum duration, fixed amount of memory, and no persistent local storage.These constraints make it challenging for data-intensive workloads to take advantage of serverless because they lead to sharing significant amounts of data through remote storage.In this paper, we build a performance model for serverless workloads that considers how data is shared between functions, including the amount of data and the underlying technology that is being used.The model's accuracy is assessed by running a real workload in a cluster using Knative, a state-of-theart serverless environment, showing a relative error of 5.52%.With the proposed model, we evaluate the performance of data-intensive workloads in serverless, analyzing parallelism, scalability, resource requirements, and scheduling policies.We also explore possible solutions for the data-sharing problem, like using local memory and storage.Our results show that the performance of data-intensive workloads in serverless can be up to 4.32× faster depending on how these are deployed.
R2-D2: A Modular Baseline for Open-Domain Question Answering

This work presents a novel four-stage open-domain QA pipeline R2-D2 (Rank twice, reaD twice). The pipeline is composed of a retriever, passage reranker, extractive reader, generative reader and a mechanism that aggregates the final prediction from all system’s components. We demonstrate its strength across three open-domain QA datasets: NaturalQuestions, TriviaQA and EfficientQA, surpassing state-of-the-art on the first two. Our analysis demonstrates that: (i) combining extractive and generative reader yields absolute improvements up to 5 exact match and it is at least twice as effective as the posterior averaging ensemble of the same models with different parameters, (ii) the extractive reader with fewer parameters can match the performance of the generative reader on extractive QA datasets.
Late Breaking Results: Physical Adversarial Attacks of Diffractive Deep Neural Networks

Diffractive Deep Neural Network $(\mathrm{D}^{2}$ NN) can work as a neural network with the diffraction of light and have demonstrated orders of magnitude performance improvements in computation speed and energy efficiency [1], [2]. As a result, there have been increasing interests in applying $\mathrm{D}^{2}$ NNs into security-sensitive applications, such as security gate sensing, drug detection, etc. However, the comprehensive vulnerability and robustness of optical neural networks have never been studied. In this work, we develop the first adversarial attack formulations over optical physical meanings, and provide comprehensive analysis of adversarial robustness of $\mathrm{D}^{2}$ NNs under practical adversarial threats over optical domains, i.e. Phase attack, Amplitude attack, and Complexdomain attack, which can be realized in $\mathrm{D}^{2}$ NN system using amplitude and phase modulators. We demonstrate that the proposed Complex Fast Gradient Sign Method (Complex-FGSM) can successfully generate minimal-changed (small epsilon) physically feasible adversarial examples targeting pre-trained $\mathrm{D}^{2}$ NNs model on MNIST-10 dataset, which bring down its accuracy to $\le 20$% from 95.4%.
Direct assessment of individual connotation and experience

We introduce cognitive-affective maps (CAMs) as a novel tool to assess individual experiences and belief systems. CAMs were first presented by the cognitive scientist and philosopher Paul Thagard as a graphical representation of a mental network, visualizing attitudes, thoughts, and affective connotations toward the topic of interest. While CAMs were originally used primarily to visualize existing data, the recent release of the new software tool Valence has facilitated the applicability of CAMs for empirical data collection. In this article, we explain the concept and the theoretical background of CAMs. We exemplify how CAMs can be applied in research practice, including different options for analysis. We propose CAMs as a user-friendly and versatile methodological bridge between qualitative and quantitative research approaches and encourage incorporating the method into studies to access and visualize human attitudes and experience.
Formal description of blocks of the objectives tree based on the fuzzy logic apparatus

The article analyzes the automated formation of a tree of goals of a production enterprise. The hierarchical structure of the elements is quite complex for direct visual perception, and the choice and variation of the parameters of these links does not reflect the obvious relationship between the characteristics of the links of the decomposed chain, which, in turn, makes it impossible to assess the impact of each element on achieving both global and local goals. In addition, not all the obtained criteria have an unambiguous quantitative assessment - they are often represented by a quantitative indicator, which makes it difficult to apply any mathematical methods and algorithms. To solve this problem, it is proposed to use the methods of the fuzzy logic apparatus, which can be used to formalize the parameters of the tree of goals of a production enterprise. This technique will allow us to obtain quantitative data on the qualitative characteristics of the production enterprise (which can be used in various methods of mathematical optimization).
Data breach recovery areas: an exploration of organization's recovery strategies for surviving data breaches

Purpose Data breaches are an increasing phenomenon in today's digital society. Despite the preparations an organization must take to prevent a data breach, it is still necessary to develop strategies in the event of a data breach. This paper explores the key recovery areas necessary for data breach recovery. Design/methodology/approach Stakeholder theory and three recovery areas (customer, employee and process recovery) are proposed as necessary theoretical lens to study data breach recovery. Three data breach cases (Anthem, Equifax, and Citrix) were presented to provide merit to the argument of the proposed theoretical foundations of stakeholder theory and recovery areas for data breach recovery research. Findings Insights from these cases reveal four areas of recovery are necessary for data breach recovery – customer recovery, employee recovery, process recovery and regulatory recovery. Originality/value These areas are presented in the data recovery areas model and are necessary for: (1) organizations to focus on these areas when resolving data breaches and (2) future data breach recovery researchers in developing their research in the field.
Extended Graph of the Fuzzy Topographic Topological Mapping Model

Fuzzy topological topographic mapping (FTTM) is a mathematical model which consists of a set of homeomorphic topological spaces designed to solve the neuro magnetic inverse problem. A sequence of FTTM, FTTMn, is an extension of FTTM that is arranged in a symmetrical form. The special characteristic of FTTM, namely the homeomorphisms between its components, allows the generation of new FTTM. The generated FTTMs can be represented as pseudo graphs. A graph of pseudo degree zero is a special type of graph where each of the FTTM components differs from the one adjacent to it. Previous researchers have investigated and conjectured the number of generated FTTM pseudo degree zero with respect to n number of components and k number of versions. In this paper, the conjecture is proven analytically for the first time using a newly developed grid-based method. Some definitions and properties of the novel grid-based method are introduced and developed along the way. The developed definitions and properties of the method are then assembled to prove the conjecture. The grid-based technique is simple yet offers some visualization features of the conjecture.