Reviewerly/exHarmony huggingface dataset:
Research and Implementation of Biomimetic Cluster Method for UAVs Anti-collision

The anti-collision method of UAV cluster oriented to local space is constructed based on the behavior mode of biological group. This paper mainly studies the anti-collision of UAV in the process of individual anti-collision, formation configuration anti-collision, and group formation reconstruction anti-collision in case of sudden obstacle invasion, and finally establishes the anti-collision model of UAV cluster, and constructs the algorithm flow of its implementation. The simulation results show that the algorithm in this chapter can not only ensure the safe distance between each UAV in the group, but also ensure the group flight safety in the formation process of UAV, and can deal with the situation of sudden obstacle invasion, greatly improving the formation flight safety. Finally, the feasibility of the algorithm is proved by the UAV flight test in the actual environment.
An Iterative Hybrid Agile Methodology for Developing Archiving Systems

With the massive growth of the organizations files, the needs for archiving system become a must. A lot of time is consumed in collecting requirements from the organization to build an archiving system. Sometimes the system does not meet the organization needs. This paper proposes a domain-based requirement engineering system that efficiently and effectively develops different archiving systems based on new suggested technique that merges the two best used agile methodologies: extreme programming (XP) and SCRUM. The technique is tested on a real case study. The results shows that the time and effort consumed during analyzing and designing the archiving systems decreased significantly. The proposed methodology also reduces the system errors that may happen at the early stages of the development of the system.
On the structure of $1$-generator quasi-polycyclic codes over finite chain rings

Quasi-polycyclic (QP for short) codes over a finite chain ring $R$ are a generalization of quasi-cyclic codes, and these codes can be viewed as an $R[x]$-submodule of $\mathcal{R}_m^{\ell}$, where $\mathcal{R}_m:= R[x]/\langle f\rangle$, and $f$ is a monic polynomial of degree $m$ over $R$. If $f$ factors uniquely into monic and coprime basic irreducibles, then their algebraic structure allow us to characterize the generator polynomials and the minimal generating sets of 1-generator QP codes as $R$-modules. In addition, we also determine the parity check polynomials for these codes by using the strong Gr\"{o}bner bases. In particular, via Magma system, some quaternary codes with new parameters are derived from these 1-generator QP codes.
Self-Teaching Machines to Read and Comprehend with Large-Scale Multi-Subject Question-Answering Data

In spite of much recent research in the area, it is still unclear whether subject-area question-answering data is useful for machine reading comprehension (MRC) tasks. In this paper, we investigate this question. We collect a large-scale multi-subject multiple-choice question-answering dataset, ExamQA, and use incomplete and noisy snippets returned by a web search engine as the relevant context for each question-answering instance to convert it into a weakly-labeled MRC instance. We then propose a self-teaching paradigm to better use the generated weakly-labeled MRC instances to improve a target MRC task. Experimental results show that we can obtain +5.1% in accuracy on a multiple-choice MRC dataset, C^3, and +3.8% in exact match on an extractive MRC dataset, CMRC 2018 over state-of-the-art MRC baselines, demonstrating the effectiveness of our framework and the usefulness of large-scale subject-area question-answering data for different types of machine reading comprehension tasks.
Prediction Meaning of Words with Word2Vec on Whatsapp Data for Disaster Topic

The community’s need for conditions that are safe from disasters is the basis for the problems of the system to be created. The system that will be created is able to detect the meaning of words from Whatsapp data obtained from the public. The data used is Whatsapp social media data, systematics of data processing starts from pre-processing and ends with an extended factorization matrix Word2Vec analysis, which is called Continuous Bag-of-Word (CBOW) to get the meaning of sentences as early detection of disaster locations. The system can extract 43% required Whatsapp data in the total of data processed by the system, and total accuracy on Word2Vec is 79%.
MACHINE LEARNING APPLICATION: IMAGE PROCESSING

-In the current scenario of the data world, the data holds significant information if processed correctly. The data can be in the form of images which can prove to be a boon in deriving the useful insights from it in order to get the knowledge of things at an early stage itself. But the matter of concern is deriving the information from the images will be a tedious task for human beings and would incur a heavy cost and time. So, an easy and cheaper technique is to teach a machine efficiently to do the task for us. The concept of using Machines to do human tasks is known as Machine Learning. In this paper, I present various literature reviews regarding image processing in Machine learning and how image processing has helped in identifying the issues at early stages so that they can be resolved easily without causing much harm. Also, image processing has been a helpful tool in computer vision.
HUBUNGAN PEMBELAJARAN ONLINE DENGAN MOTIVASI BELAJAR SISWA KELAS V SD NEGERI 12 MANURUNGE KECAMATAN TANETE RIATTANG KABUPATEN BONE

The problem in this research is the lack of student motivation towards online learning. This research is a quantitative study with a correlation design which aims to determine 1. the description of the online learning process at SD Negeri 12 Manuruge, the relationship between online learning and the learning motivation of fifth grade students at SD Negeri 12 Manuruge. 2. An overview of the learning motivation of fifth grade students at SD Negeri 12 Manuruge. 3. The relationship between online learning and learning motivation of fifth grade students at SD Negeri 12 Manuruge. The results showed that there was a significant relationship between online learning and the learning motivation of fifth grade students at SD 12 Manuruge with a t hitung value (4.1375) greater (>) t table value (0.671953) which means H0 is rejected and H1 is accepted. Based on the analysis of the data above, it can be concluded that the online learning process will have an impact on the mindset of students in learning so that it will affect their learning motivation in the classroom. So, the better the online learning process, the better the students' learning motivation.
Math Word Problem Generation with Mathematical Consistency and Problem  Context Constraints

We study the problem of generating arithmetic math word problems (MWPs) given a math equation that specifies the mathematical computation and a context that specifies the problem scenario. Existing approaches are prone to generating MWPs that are either mathematically invalid or have unsatisfactory language quality. They also either ignore the context or require manual specification of a problem template, which compromises the diversity of the generated MWPs. In this paper, we develop a novel MWP generation approach that leverages i) pre-trained language models and a context keyword selection model to improve the language quality of the generated MWPs and ii) an equation consistency constraint for math equations to improve the mathematical validity of the generated MWPs. Extensive quantitative and qualitative experiments on three real-world MWP datasets demonstrate the superior performance of our approach compared to various baselines.
Catalog of Coefficients for Estimating Bulk and Shear Moduli as a Function of Lithology

The purpose of this paper is to present correlation coefficients for a variety of rock types that can be used in a suitable petroelastic model (PEM). The correlation coefficients for different rock types facilitate the application of a petroelastic model in reservoir flow models. By combining the correlation coefficients and the PEM, it is possible to obtain low-cost estimates of reservoir geophysical attributes. The rock types include dolomite, limestone, high porosity sandstone, poorly consolidated sandstone, tight gas sandstone, and well consolidated Gulf Coast sandstone.
Thoreau and the Idea of John Brown: The Radicalization of Transcendental Politics

Research Article| October 01 2021 Thoreau and the Idea of John Brown: The Radicalization of Transcendental Politics Ilkin Huseynli Ilkin Huseynli University of Milan Search for other works by this author on: This Site Google The Pluralist (2021) 16 (3): 112–125. https://doi.org/10.5406/pluralist.16.3.0112 Cite Icon Cite Share Icon Share Facebook Twitter LinkedIn MailTo Permissions Search Site Citation Ilkin Huseynli; Thoreau and the Idea of John Brown: The Radicalization of Transcendental Politics. The Pluralist 1 January 2021; 16 (3): 112–125. doi: https://doi.org/10.5406/pluralist.16.3.0112 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All Scholarly Publishing CollectiveUniversity of Illinois PressThe Pluralist Search Advanced Search The text of this article is only available as a PDF. Copyright 2021 by the Board of Trustees of the University of Illinois2021 Article PDF first page preview Close Modal You do not currently have access to this content.
A Comparison of Multiple Selections Using Multiple Checkbox Selections and List Boxes

Website forms are commonly used for collecting information from users. Users choose one or more alternatives from a list. However, data input can be time-consuming. We therefore set out to explore which multiple selection method is the fastest and perceived most positively; the ones relying on multiple checkboxes or list boxes. An experiment with 24 participants was conducted involving 20 multiple selection tasks. The result shows that the mean response time to select three options in each question of multiple checkboxes was faster and more positively perceived than multiple list boxes. The results thus suggest that web developers should use multiple checkboxes instead of multiple list boxes.
Neural Stochastic Dual Dynamic Programming

Stochastic dual dynamic programming (SDDP) is a state-of-the-art method for solving multi-stage stochastic optimization, widely used for modeling real-world process optimization tasks. Unfortunately, SDDP has a worst-case complexity that scales exponentially in the number of decision variables, which severely limits applicability to only low dimensional problems. To overcome this limitation, we extend SDDP by introducing a trainable neural model that learns to map problem instances to a piece-wise linear value function within intrinsic low-dimension space, which is architected specifically to interact with a base SDDP solver, so that can accelerate optimization performance on new instances. The proposed Neural Stochastic Dual Dynamic Programming ($\nu$-SDDP) continually self-improves by solving successive problems. An empirical investigation demonstrates that $\nu$-SDDP can significantly reduce problem solving cost without sacrificing solution quality over competitors such as SDDP and reinforcement learning algorithms, across a range of synthetic and real-world process optimization problems.
Construction Method of Enterprise-level Database Operational Measurement Platform Based on Bidirectional Coupling Algorithm

When identifying the enterprise database data information, the enterprise database operational dimension measurement platform based on the bilinear method will produce ringing or overshoot effect, the database data monitoring effect is poor as well as the accuracy of operation measurement. To solve this problem, this paper proposes a method to build an enterprise level database operation and maintenance measurement platform based on bidirectional coupling algorithm. Build enterprise database operation measurement platform. The enterprise database monitoring platform is connected with the monitoring database by remote database chain. The Oracle database job scheduling method is used to obtain the monitoring index information in the monitored database, and the database memory performance is comprehensively evaluated by MPI (Memory Perform Index). The platform uses semantic capture layer and related analysis layer to distinguish user behavior, analyze user experience satisfaction, and realize the operation measurement of enterprise level database. The experimental results show that the operational measurement platform built by this method has high throughput, low memory occupancy rate, high measurement accuracy and good user experience.
Three-dimensional Motion Skeleton Reconstruction Algorithm for Gymnastic Dancing Movements

Aiming at the problem of inaccurate matching results in the traditional three-dimensional reconstruction algorithm of gymnastic skeleton, a three-dimensional motion skeleton reconstruction algorithm of gymnastic dance action is proposed. Taking the center of gravity of the human body as the origin, the position of other nodes in the camera coordinate system relative to the center point of the human skeleton model is calculated, and the human skeleton data collection is completed through action division and posture feature calculation. Polynomial density is introduced into the integration of convolution surface, and the human body model of convolution surface is established according to convolution surface. By using the method of binary parameter matching, the accuracy of the matching results is improved, and the three-dimensional skeleton of gymnastic dance movement is reconstructed. The experimental results show that the fitting degree between the proposed method and the actual reconstruction result is 99.8%, and the reconstruction result of this algorithm has high accuracy.
Penerapan Aplikasi Microsoft Mathematics pada Pembelajaran Matematika bagi Siswa SMKS-9 Muhammadiyah Kota Bengkulu

This research is conducted based on observation that learning about mathematics in the class is always use convensional method. This research is sopposed to know learning outcomes in the application of learning Microsoft Mathematics application student SMKS - 9 Muhammadiyah Bengkulu. The methodology that is use in the research is Eksperimen Design with Randomized Control Group Only Design. The sample of this research is 81 student. Sample class is XI-2 and kelas XI-1 as an eksperimen class and control class. The data obtained were analyzed using thr t-test. Based on the result of the study, The average value of student understanding of learning outcomes by Microsoft Mathematics is 60,83 , while the conventional learning model is 48,29, value t = 2,67 and t = 1,65
Pengembangan Sistem Informasi Data Penduduk Desa Cengal

Population data processing is a main activity carried out by the Government Section of Cengal Village, Maja District, Majalengka Regency on an ongoing basis. Where the current system often finds difficulties when searching for population data, duplication of data is found as a result of ordered population data collection and the occurrence of damage and data loss as a result of the existing storage media only in the form of documents. The purpose of this research is to design a population data processing application in Cengal village. The system development method used is the waterfall method with PHP and MYSQL scripts as database managers. This population data application is designed and built in order to help facilitate the search for population data and processing population data regarding births, deaths, immigrants and relocations, reducing the occurrence of damage and loss of population data due to storage media in the form of documents and can shorten operational time. With the storage media in the form of a good database, it can improve the performance of the government section in serving and presenting population information to the public so that it is better, faster and the application used can provide convenience to the user
A Hierarchical Gamma Mixture Model Toward Hidden Markov Random Field for High-Resolution SAR Image Segmentation

Accurately modeling the distributions of spectral intensities is an effective way to obtain accurate segmentation results. Existing mixture models mostly fail to establish accurate statistical models of high-resolution SAR images due to the complex statistical features of their spectral intensities. We propose a hierarchical gamma mixture model (HGaMM)-based high-resolution SAR images segmentation algorithm. In the proposed algorithm, a statistical model for SAR images is built using HGaMM, which can model heavy-tailed, asymmetric, multimodal, or flat distributions with its hierarchical structure. The algorithm allows for the effective utilization of spectral intensity information required for image segmentation. The HGaMM consists of several components that serve as the first layer, and several elements under each component serve as the second layer. The component weight is constructed using posterior probabilities of local pixels aimed at reducing noise effects. The segmentation model can then be established by the Bayesian theorem. A new expectation maximization/adding or deleting Markov chain Monte Carlo (EM/ADMCMC) is incorporated to implement parameter estimation and determine the optimum number of components. Several experiments were implemented on simulated and real high-resolution SAR images to evaluate the applicability and efficiency of the proposed approach. The experimental results show that the proposed HGaMM algorithm outperforms traditional algorithms and can obtain accurate results.
On the effectiveness of Gated Echo State Networks for data exhibiting long-term dependencies

In the context of recurrent neural networks, gated architectures such as the GRU have contributed to the development of highly accurate machine learning models that can tackle long-term dependencies in the data. However, the training of such networks is performed by the expensive algorithm of gradient descent with backpropagation through time. On the other hand, reservoir computing approaches such as Echo State Networks (ESNs) can produce models that can be trained efficiently thanks to the use of fixed random parameters, but are not ideal for dealing with data presenting long-term dependencies. We explore the problem of employing gated architectures in ESNs from both theoretical and empirical perspectives. We do so by deriving and evaluating a necessary condition for the non-contractivity of the state transition function, which is important to overcome the fading-memory characterization of conventional ESNs. We find that using pure reservoir computing methodologies is not sufficient for effective gating mechanisms, while instead training even only the gates is highly effective in terms of predictive accuracy.
Quadrature entanglement in the microwave domain with a phase locking protocol

Abstract Entanglement has attracted great attention in the past few decades due to its potential applications in the field of quantum information protocols. From now, achieving excellent phase locking in entanglement generation is significant yet a challenging task in cryogenic quantum technology. In this work, we propose and demonstrate a comprehensive paradigm of phase locking protocol for quadrature entanglement at the microwave wavelengths. We carry out a theoretical derivation of the quadrature entangled microwaves generated based on Josephson parametric amplifiers (JPAs), and the phase locking error signal, which is used to lock the relative phase of zero between the two quadrature squeezed microwaves. Simulating data for the phase locking scheme are shown under different parameter settings for comparison. Finally, we use the proposed phase locking scheme to enable a stable output of quadrature entangled microwave.
Implementasi Algoritma Apriori dalam Meningkatkan Strategi Penjualan pada Toko Miring

Toko Miring is one of the stores that sell various daily completeness needs such as food, drinks, household appliances, and others placed on shelves or storefronts. Each sales transaction data is recorded in a database system through the cashier application. According to Rahmat Fathi, supervisor at Toko Miring, at the beginning of 2020 precisely in March, The Tilt store experienced a decrease in the number of sales transactions compared to the previous year, this is because the buying and selling process in this Tilt store has many problems, one of which is the storage of sales data is still in writing. This causes buyers to have difficulty finding the desired product because the preparation of the product is not separated by type. From the problems that occurred, researchers together with Toko Miring proposed to rearrange a sales strategy to increase sales at Tilt Stores. In devising sales strategies, researchers utilize sales transaction data in previous years to be reprocessed using data mining techniques. Based on the description above, the researcher will analyze sales transaction data using data mining techniques by implementing a priori algorithms with a support value and confidence value of 50% and implementing it into the python programming language. The result of this study is that researchers managed to get sales patterns that can improve sales strategies that produce information that is useful for related parties in making sales strategy decisions such as sales packages for promos, recommending products to customers and maintaining product availability in order to increase sales intensity in some stores such as in Tilt Stores.
Optimized quantum singular value thresholding algorithm based on a hybrid quantum computer

Quantum singular value thresholding (QSVT) algorithm, as a core module of many mathematical models, seeks the singular values of a sparse and low rank matrix exceeding a threshold and their associated singular vectors. The existing all-qubit QSVT algorithm demands lots of ancillary qubits, remaining a huge challenge for realization on near-term intermediate-scale quantum computers. In this paper, we propose a hybrid QSVT (HQSVT) algorithm utilizing both discrete variables (DVs) and continuous variables (CVs). In our algorithm, raw data vectors are encoded into a qubit system and the following data processing is fulfilled by hybrid quantum operations. Our algorithm requires O [log( MN )] qubits with O (1) qumodes and totally performs O (1) operations, which significantly reduces the space and runtime consumption.
Data Mining, Linked Data, and Library Service Delivery

There is no doubt that data mining and linked data can enhance library service delivery. Data mining aspects such as text and image mining will enable libraries to have access to data that can be used to discover new knowledge aid planning for effective service delivery or service improvement. Also, linked data will enable libraries connect with other libraries to share such data that can enhance job performance leading to enhanced productivity, improved service delivery, and wider visibility and access to library resources.