Reviewerly/exHarmony huggingface dataset:
Poisson multi‐Bernoulli mixture filters with coloured measurement noise

To solve multitarget tracking (MTT) problems with coloured measurement noise, this study proposes a Poisson multi-Bernoulli mixture filter with coloured measurement noise (PMBM-CMN) and a robust PMBM-CMN filter. By using the measurement differencing method and state augmentation approach, the proposed PMBM-CMN filter transforms a state estimation problem with coloured measurement noise into a problem with white measurement noise. However, covariances of the true process and measurement noise in the proposed PMBM-CMN filter are time-varying and unknown, which may degrade the filtering performance. Therefore, a robust PMBM-CMN filter is proposed for estimating the augmented state, including the kinematic state, the predicted state covariance, and the white measurement noise covariance. For linear Gaussian systems, the augmented state is modelled as a Gaussian inverse Wishart inverse Wishart (GIWIW) distribution. The variational Bayesian method is also employed to guarantee the conjugacy of the GIWIW density. Simulation results demonstrate the ability of the PMBM-CMN filter to solve MTT problems with coloured measurement noise and show that the robust PMBM-CMN filter based on the GIWIW model (GIWIW-PMBM-CMN) has the best overall performance in comparison with existing state-of-the-art filters.
Analog-Digital Quantum Simulations with Trapped Ions

The 53rd Annual Meeting of the APS Division of Atomic, Molecular and Optical Physics will take place from May 30 – June 3, 2022 in Orlando, Fl, USA. <a href="https://morressier.zoom.us/j/89880865272?pwd=SWxTRGhra1ZOSDBrSkhVZDBzVDR6UT09/">Virtual Presenter Help Desk</a>
Three-photon entanglement transfer and control in multi-emitter chiral waveguide quantum electrodynamics

The 53rd Annual Meeting of the APS Division of Atomic, Molecular and Optical Physics will take place from May 30 – June 3, 2022 in Orlando, Fl, USA. <a href="https://morressier.zoom.us/j/89880865272?pwd=SWxTRGhra1ZOSDBrSkhVZDBzVDR6UT09/">Virtual Presenter Help Desk</a>
Current Status of General Education Related to Dance and Physical Education at Korean Universities

This study aims to analyze general education subjects to improve the common core competencies required of college students through general education in the fields of dance and physical education. To this end, the current status of general education related to dance and physical education in 15 four-year colleges with dance departments among universities in Seoul was examined using the objective sampling method. First, the information disclosed on each university’s website was mainly investigated, then supplementary data were obtained from the person in charge of the general education department of each university. As a result of the analysis, the general education courses offered at 15 universities can be broadly divided into the following three categories: First, common compulsory subjects that all students must complete; second, core and central subjects that are characteristic courses to be completed by all students and that contain the values pursued by each university; and third, general subjects that can be freely selected and completed. Most of the dance and physical education subjects are offered as elective subjects, rather than compulsory or core subjects. In addition, many universities that have dance departments do not offer any general education subjects related to dance at all. Overall, general education related to dance and physical education was biased toward theory classes or operated mainly on simple practice.
Multi-mode Ground-state Cooling of a Trapped Ion Chain beyond the Lamb-Dicke Regime

The 53rd Annual Meeting of the APS Division of Atomic, Molecular and Optical Physics will take place from May 30 – June 3, 2022 in Orlando, Fl, USA. <a href="https://morressier.zoom.us/j/89880865272?pwd=SWxTRGhra1ZOSDBrSkhVZDBzVDR6UT09/">Virtual Presenter Help Desk</a>
Moving scene-based video segmentation using fast convolutional neural network integration of VGG-16 net deep learning architecture

Video and object segmentation are considered significant research topics in image-video processing. The techniques and mathematical models involved in detecting and segmenting objects employ several modules of different high-level approaches developed for video analysis, object extraction, classification, and recognition. Moving object detection is important in various applications like video surveillance, moving object tracking. This paper proposes video segmentation of moving scene using fast convolutional neural network with VGG-16 net architecture which improves the accuracy. This developed method based on CNN sparsely represents foreground, background, and segmentation mask, which is used in reconstructing the original images. The feed-forward network-trained videos are applied for object detection in a single image with co-segmentation approach where videos or image collections are required as the input. The segmentation is performed through comparative analysis of real-time DAVIS dataset. The results of the experiment show the efficiency of this proposed method which is tested and compared with the existing techniques such as convolution neural network, [Formula: see text]-nearest neighbors, and artificial neural network by the parameters, namely accuracy, precision, recall, and F1-Score. The proposed technique has been improved in terms of accuracy by 97.8%, precision by 94%, recall by 87.9%, and F1-Score by 83.8%.
Phase transition in a nonlinear optical Ising machine

The 53rd Annual Meeting of the APS Division of Atomic, Molecular and Optical Physics will take place from May 30 – June 3, 2022 in Orlando, Fl, USA. <a href="https://morressier.zoom.us/j/89880865272?pwd=SWxTRGhra1ZOSDBrSkhVZDBzVDR6UT09/">Virtual Help Desk</a>
Summer Skybox: A Device Representing the Sky for Personalized Day Cycle

People are psychologically impacted by the unavoidable changes in sunrise and sunset times over a year. When people work remotely, the only natural cue that their room provides comes from the few windows; working at the office provides many more cues. To offset this deficiency, people use clocks and focus timers. They rely on abstract numbers that make them feel disconnected from nature. We present Summer Skybox, a device that displays a personalized day cycle using sun and sky color to represent time. A field study with six international students showed that Summer Skybox is helpful on shorter winter days. Additionally, some participants sensed a connection to their hometown and could better perceive the passage of time. Our findings confirm that experiencing the changes in daylight over a year does impact humans psychologically, and that our solution provides an effective way of representing the daylight cycle using natural cues.
E-Teach Performance: A Scale to Evaluate Teaching Performance in Virtual Environments in Higher Education Programs for Adults

The study proposes an instrument to evaluate teaching performance in virtual environments (E-Teach Performance), identifying 17 items, which makes it an easy-to-apply instrument for evaluating teacher performance that provides online teaching. The pilot test consisted of 222 participants who belong to a sector of working adult students who stated that they were enrolled during the first semester of 2021. The items were formulated after having carried out an exhaustive review of the literature to later be evaluated by experts who guaranteed the content of the construct. Exploratory and confirmatory factor analysis were applied to evaluate the proposed model, the results allowed to know a scale of two dimensions which were called: (1) Virtual Teaching Collaboration and (2) Virtual Teaching Accompaniment. The statistical analyzes showed a good fit of the model; in this way, this scale allows the authors to provide the scientific and university community with a reliable instrument to be applied both in future research and to evaluate university teachers who work in virtual environments.
Comparison of Quantum Entangling Gates for Ions in Penning and RF Paul Traps

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Evaluation of Sentiment Analysis of Text Using Rule-Based and Automatic Approach

The technique of determining whether a text is good, negative or neutral is known as sentiment analysis (SA).Sentiment Analysis can be identified by many names like Textual Analysis, Opinion Mining. Sentiment Analysis is a branch of Natural Language Processing (NLP) that focuses on the expression of subjective views and feelings about a topic gathered from multiple sources. Sentiment Analysis is a collection of methods for detecting and extracting opinions and uses them for the benefit of business operation. It is a classification algorithm aimed at finding opinions and decision-making point of view. Sentiment Analysis is performed in many ways, Automatic classification approach involves Nave Bayes (NB), Support Vector Machine (SVM), and Linear Regression is examples of supervised machine learning methods (LR). The data is explored using unsupervised machine learning. Recurrent Neural Network (RNN) derivatives are also used for classification. Rule-based approach involves various NLP process for classification.
Performance Evaluation of SHA-256 and BLAKE2b in Proof of Work Architecture

The popularity of blockchain, which is a technology with a distributed architecture, is increasing day by day due to the advantages it provides. Along with blockchain, interest in high-performance and efficient hash algorithm applications is increasing rapidly. While considering the amount of power and environmental problems required for these applications, more efficient algorithms were needed. In this study, a comparison of SHA-256 and BLAKE2b, one of the most popular algorithms, is presented. In the experiments, a standard Intel i5 based computing system is used. The benchmarking approach focuses on computationally heavy processes such as Proof of Work and Merkle Tree. This article presents a comparison of these two algorithms in a Bitcoin-like mining architecture.
Research on multimodal human-computer interaction technology based on audiovisual fusion

With the improvement of information technology, service robots are becoming more and more deeply involved in our work and life, and provide a wider variety of services. How to make robots communicate with humans more intelligently and understand and accomplish people's task needs has become a current research hotspot. In this paper, we propose a multimodal human-robot interaction technique with D-S evidence theory for fusion of audiovisual information, and a multimodal interaction system with speech and gesture interaction to improve the performance of human-robot interaction.
Faculty Evaluation System Platform with Decision Support Mechanism

The evaluation process to teachers is a method to ensure that instruction quality has been delivered to various stakeholders. Assessing the performance of faculty members is now an integral part of the educational system as it aims to assess faculty performance using sets of criteria and provide necessary academic intervention program anchored in the development of comprehensive faculty development program. With this, the researchers developed faculty evaluation system integrating decision support mechanism that can provide automatic report generation in terms of evaluation. In addition, a rule-based engine has been integrated as a mean to provide decision support mechanism in terms of automatic development plan. The evolutionary prototyping model was used in the development of the system. Based on expert evaluation result, using the ISO selected criteria, the system recorded a grand mean 4.31 which has an interpretation of “Very Satisfactory” This means that the system is ready for deployment.
On the Development and Intelligent Application of Computer Software Technology in the Big Data Era

With the increasing economic level of our country, our country's overall comprehensive national strength has gradually improved, especially in the high-tech industries, and the overall technological level has been steadily at the forefront of the world. In the current high-tech industry, computer technology has shown a more prominent role than other technologies. This article analyzes the important role and core technology in the development and intelligent application of computer software technology in the era of big data. It introduces virtualization technology, network cloud storage technology, information security technology and network communication technology, and introduces domestic the application situation is explained, and some problems in the application process are also analyzed accordingly. The results show that the intelligence has increased by 6.7%.
Justice Information Sharing Training and Technical Assistance Program (DOJ)

Federal Grants & ContractsVolume 46, Issue 13 p. 6-6 Grants alerts Justice Information Sharing Training and Technical Assistance Program (DOJ) First published: 30 May 2022 https://doi.org/10.1002/fgc.32413Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume46, Issue132 June 2022Pages 6-6 RelatedInformation
Finding Customer Patterns Using FP-Growth Algorithm for Product Design Layout Decision Support

The transaction database contains a very large and irregular dataset that requires another mechanism to read it, even though there is a lot of new knowledge that can be revealed, including associations or relationships between goods or products that are often purchased by customers.The new finding of the relationship between these variables is usually called association rule mining.The algorithm that is developing and often used is frequent pattern-growth (FP-Growth).The problem of very many transaction databases also occurred in Mr. A. So, in this research, we will look for customer patterns using the FP-Growth algorithm.The algorithm aims to find the maximum frequent itemset.The frequent itemset will be generated into associative rules so that it becomes valuable new knowledge.This knowledge can be used as a reference and consideration in making decisions.The FP-Growth algorithm will be implemented using the rapidminer tools on the transaction data of Mr.A's goods sales.The pattern of rules that will be searched for is based on data on sales of goods transactions.The results of the study obtained six association rules with five conclusions being the gift category.So that the suggestion for decision making is to lay out items close to and around the gift category in order to improve marketing and service strategies in order to attract the attention and interest of pointers in making purchases of goods.
efficiency and encounters of online education for higher secondary school students

The COVID-19 pandemic has limited the rapid implementation of electronic learning. In any case, little is a basic experience with the common sense and issues looked at by the students. Consequently, this review revolves around assessing the appropriateness of web browsing and the issues it presents to students' abilities to learn. This audit used a significant assessment configuration using a review overview, facilitated by 99 students from an auxiliary school in Jasin, Melaka. Information was clearly broken down (rate evaluation and overt repetition). The announcements mostly showed that the students had computers or PDAs and a web relationship at home. In addition, the cut-off point and applicability to use computers were found to be high (&gt; 93%). Coincidentally, their inspiration in electronic learning was low (41.5%) and the capacity to work in an event was at a moderate level (66.7%). They similarly agreed that typical training (eye to eye) was great for their learning (98%). These revelations are great for the public power, principals, instructors and gatekeepers to see the significance of remarkable work environments and a consistent web relationship for productive learning.
Localization-based active learning (LOCAL) for object detection in 3D point clouds

Deep learning-based object detection and classification in 3D point clouds has numerous applications including defense, autonomous driving, and augmented reality. A challenge in applying deep learning to point clouds is the frequent scarcity of labeled data. Often, one must manually label a large quantity of data for the model to be useful in application. To overcome this challenge, active learning provides a means of minimizing the manual labeling required. The crux of active learning algorithms is defining and calculating the potential added “value” of labeling each unlabeled sample. We introduce a novel active learning algorithm, LOCAL, with an anchorbased object detection architecture, a modified object matching strategy, and an acquisition metric designed for object detection in any dimension. We compare the performance of common acquisition functions to our novel metric that utilizes all of the model outputs—including both bounding box localizations and softmax classification scores—to capture both the classification and spatial uncertainty in the model. Finally, we identify opportunities for further exploration, such as alternative measures of spatial uncertainty as well as increasing the stochasticity of the model in order to improve robustness of the algorithm.
Variants of the flower pollination algorithm for inversion of schlumberger sounding curve

Abstract Flower pollination algorithm (FPA) is a nature-inspired algorithm that mimics flowering plant pollination behavior. Yang created the FPA in 2012, and it has since proven to be superior to other metaheuristic algorithms. Many FPA variants have recently been developed through modification and hybridization. This paper provides FPA variants consisting of Modified Flower Pollination Algorithm (MFPA), elitism Flower Pollination Algorithm (eFPA), Dimension by Dimension Improvement Flower Pollination Algorithm (DDIFPA) and Flower Pollination Algorithm with Bee Pollinator (BPFPA). Validation of the code that has been created is done through simple model testing. All algorithms provide inversion results that are consistent with true value. The results of two synthetic models indicate that eFPA is the only algorithm that can reach the global optimum. Besides having the best level of accuracy, eFPA also has the best stability.
Apriori Algorithm Based Component Classifications and Adaptation for Software Reuses

With the growth in software development, the software user expects high-quality software services with lesser time complexity. Therefore, the demand for software component based development is also increasing. In the recent past, a good number of research attempts have tried to build an automated framework for generating the recommendations for component equivalence. In software engineering and program building, reusability is the utilization of existing resources in some structure inside the product items. Adaptive software reuse products improve the life cycle of software and enhance the planning, coding, and documentation in different approaches of reusability. On the other hand, reusability can be used to meet with new software demands. Thus, the proposed novel approach to Apriori based component classifications and adaptation is used to software component reusability based on the recommendation of rule-based analysis.
The World Needs Environmental and Water Resource Engineers

Journal AWWAVolume 114, Issue 5 p. 86-87 Researcher to Researcher The World Needs Environmental and Water Resource Engineers Xuyen Mai, Corresponding Author Xuyen Mai xduymai@gmail.com Search for more papers by this author Xuyen Mai, Corresponding Author Xuyen Mai xduymai@gmail.com Search for more papers by this author First published: 31 May 2022 https://doi.org/10.1002/awwa.1927Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume114, Issue5June 2022Pages 86-87 RelatedInformation
Quantum error correction in a time-dependent transverse-field Ising model

We describe a simple quantum error correcting code built out of a time-dependent transverse-field Ising model. The code is similar to a repetition code, but has two advantages: an $N$-qubit code can be implemented with a finite-depth spatially local unitary circuit, and it can subsequently protect against both $X$ and $Z$ errors if $N\ensuremath{\ge}10$ is even. We propose an implementation of this code with ten ultracold Rydberg atoms in optical tweezers, along with further generalizations of the code.
High assurance state machine microprocessor concept: Aberdeen Architecture

The Aberdeen Architecture is a high assurance microprocessor architecture concept which implements Saltzer and Schroeder’s 1975 security principles in hardware. Current microprocessors execute instructions without any verification or authentication. Saltzer and Schroeder defined fundamental security principles: complete mediation, and open design. Complete mediation means to verify access rights and authority for every operation. Protection mechanisms should be based on open design principles: protecting keys, not design secrecy. In 2002, Mann describes how secrecy makes systems brittle and subject to catastrophic failure. The Aberdeen Architecture is high assurance computer architecture based on ‘open design’ principles, complete mediation, and RISC-V instruction set architecture. Aberdeen Architecture uses several hardware state machine monitors to enforce hardware security policies for the execution pipeline. The state machines’ security policies cover instruction execution, control flow integrity, data flow integrity, and memory access integrity. The individual security policies provide overlapping coverage. The security of the whole architecture is greater than the sum of the individual parts. The Aberdeen Architecture provides near complete mediation for instruction execution. This paper presents an introduction to the Aberdeen Architecture