bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mathematic connections through scientific prezi and lectora inspire
Authors: Susilawati W , Nuraida I 
Published: April 2021

AbstractThe low quality of students in mathematical connections requires learning processes using technology-based. The purpose of the study was to analyze the ability of mathematical connections between students learning scientific based on prezi, lectora-inspire and conventional learning. This study used a quasi-experimental method and a nonequivalent control group design. Involving a sample of 84 students from three senior high school classes which were divided into an experimental group and a control group as a test instrument for mathematical connection ability. The data analysis uses independent T-test, one way ANOVA, and post hoc least square differences test. The finding is that there are differences in the improvement and achievement of mathematical connection skills between students who carry out prezi-based scientific learning, better than students who have lectora-inspiration based learning and conventional learning. Prezi and lectora-inspire scientific learning facilitates students interaction processes to construct concepts, find problems, apply media, and reflect processes so that it can improve students mathematical connection abilities.
Equivalence between categories of supermodules
Authors: Hadi I , Mahatma Y 
Published: April 2021

Abstract
Let R be commutative algebra with unit element. Given any R-module U, there exists a category CU
whose objects are all pairs (N,M) where N is direct sum of U and M contains U. It has been shown that if W is a direct summand of U then there exists a functor from CU
to CW. Further, if W and U are isomorphic then there exists category equivalence between CU
and C W
.
Modeling of West Java inflation with Spatial Durbin Model (SDM)
Authors: Santi V M , Djuaraidah A , Sumertajaya I M , Fitrianto A , Rahyu W 
Published: April 2021

Abstract
Inflation in Indonesia, in general, can be caused by a combination of various factors, both in terms of demand, supply, and expectations. The contribution of each element that influences inflation is not always the same from time to time. Gordon (1997) explains the determinants of inflation from these three sides, namely demand, supply, and expectations. The importance of expectations in influencing increase was also stated earlier by Chopra (1985). In this study, spatial Durbin for panel data with fixed effects in modeling inflation in the West Java province. The results show that Minimum Work Wage, percentage of poor population, Gross Regional Domestic Product (GRDP), spatial lag of poor population, and spatial lag of GRDP have a significant influence on the Durbins panel spatial model. There is an adjacent regional effect on inflation in the West Java province based on the value of the lambda obtained. Based on the criteria R-square of 94.29%, it can be concluded that the SDM panel data model has an excellent performance in explaining the diversity of data. The AIC and BIC criteria also show that the SDM panel data model well applied in modeling Regency/City inflation in West Java province.
Distance and displacement concept: Comprehension shifting of students on learning process
Authors: Jufriadi A , Ayu H D , Sholikhan S , Muttaqin A , Budiyono A , Sundaygara C , Hudha M N 
Published: April 2021

Abstract
This study aims to investigate comprehension shifting students of distance and displacement concept on learning process. A mixed methods approach using embedded experiments has been used to analyze the comprehension shifting of students. Quantitative and qualitative data obtained from responses to multiple-choice conceptual questions and the reasons put forward during the discussion in the learning process. The results of the analysis have shown that initially all students were not able to distinguish distances and displacements correctly. After the initial verbal discussion, about 10% of students were able to correctly distinguish distance and displacement. The discussion was continued with lecturer intervention by asking students to visualize the problem through pictures or graphics, so that all students (100%) were able to distinguish distances and displacements correctly. Furthermore, the results of the analysis of changes in student understanding can be used as a basis for educators to design learning strategies for the topics of distance and displacement in particular and kinematics in general.
Development of learning material in the form of a smartphone application on the subject of temperature and heat by inquiry-based learning (IBL) for physics high school subject
Authors: Raihanati R , Nasbey H , Astra I M , Rahma A 
Published: April 2021

Abstract
An smartphone application which are used as learning material on the subject of temperature and heat have been developed. The development process is carried out using research and development methods with reference to the Analysis-Design-Development-Development-Implementation-Evaluation development model. The novelty of learning material that was developed was to use inquiry-based learning in the writing system. In addition, the learning material which developed also includes questions that refer to higher-order thinking. The results of the validity tests show the adequacy of the learning materials with the level of difficulty of the learning materials in high school. The learning material has been tested in SMA 16 Jakarta. The test results show an increase in learning outcomes shown by the gain score of 0.4.
Characteristics of neutron diffusion coefficient as a function of energy group in the one-dimensional multi-group diffusion equation of finite slab reactor core
Authors: Shafii M A , Zakiya I , Fitriyani D , Tongkukut S H J , Abdullah A G 
Published: April 2021

Abstract
One of the most straightforward approaches that widely used to solve the neutron transport equation is the diffusion equation approach. The diffusion equation describes the individual behavior of the average neutron trajectory when interacting with matter. Usually, the neutron diffusion equation is obtained under the assumption that scattering is isotropic in the laboratory system of coordinates, and neutrons have the same energy and region is homogeneous; this called a one-speed diffusion equation. It leads to the diffusion coefficient to be independent of the spatial position; the volume of the reactor is constant, and the number density of the fuel atoms is also relatively constant. In this study, multi-group neutron diffusion characteristics were introduced in two ways. First, they vary with energy in the finite slab reactor core using a one-dimensional multi-group diffusion equation with the Gauss-Seidel iteration method. Second, using Fick’s law directly. The study used macroscopic cross-sections in the U-PuN fuel cell level as initial input for 70 energy groups. The data library used is JFS-3-J33 for 70 energy groups, which is the library data of SLAROM computer codes from JAEA Japan. The first way indicates the diffusion coefficient characteristics of U-235 and Pu-239 fuel isotopes firmly have the same pattern in each group energy. They have fluctuations throughout the fast, intermediate, and thermal energy group regions because both isotopes are fertile material. On the other hand, the diffusion coefficient of U-238 fuels isotope tends to be stable in each energy group. This event occurs because the isotope of U-238 is natural uranium, which is included as a fertile material. The second way shows the diffusion coefficients characteristics of the nuclear fuel isotopes firmly have the same pattern in each energy group, especially in the fast and thermal energy group region. They have fluctuations only throughout the intermediate energy group regions because, in this area, there is a resonant region.
A comprehensive comparison on current deep learning approaches for plant image classification
Authors: Zhou Cheng-Li , Ge Lin-Mei , Guo Yan-Bu , Zhou Dong-Ming , Cun Yu-Peng 
Published: April 2021

Abstract
Plant identification and classification play a key role in understanding, protecting and conserving biodiversity. Traditional plant taxonomy needs long time intensive training and experience, which limited others to identify plant categories. With the development of automated image-based classification, machine learning (ML) is becoming a popular tool. Image classification, especially plant images taxonomy, has achieved great improvement in these years by deep learning (DL) methods. In this study, we first reviewed current deep learning applications in the field of plant image classification, and then we tested six deep learning methods in four public plant image datasets. In order to test the classification power of DL methods at cultivar level, we prepared a Camellia sasanqua Thunb. dataset, which is called Camellia@clab, for assessing classification performance of the six DL methods. These DL models’ classification performance all exceeded 70% in the four public plant image datasets, and LeNet and DenseNet had stable good performance, with median prediction accuracy of the LeNet was over 87.29% and that of DenseNet was over 93.8% in the four public datasets at species level. At cultivar level, the lowest median prediction accuracy of those DL methods decreased to 62%, but LeNet and DenseNet still performed very well. The prediction accuracy of LeNet and DenseNet was 82.3% and 100% in the Camellia@clab dataset, respectively. DenseNet model showed a stable best classification performance among the five datasets. To our knowledge, this is the first study that provides a comprehensive review and comparison on applying current DL methods to plant image classification. This study will provide guidance for DL applications in plant image classification, and point out the protentional DL research direction for modeling improvement.
Infrared small target detection based on robust principal component analysis joint directional derivative penalty
Authors: Zhang Tiancheng , Wu Yiquan , Zhou Fei 
Published: April 2021

Abstract
Many robust principal component analysis based methods have been proposed for infrared small target detection recently. However, due to the lack of local prior information, these methods show poor performance under complex backgrounds. In this paper, a novel infrared patch-image model joint the directional derivative saliency feature is proposed. First, Laplace norm minimization is used instead of nuclear norm minimization to approximate low rank background can automatically capture the inherent rank information, which has higher matching with the real rank. Then, considering that the l
1-norm constrains target patch-image inexactly, we construct a saliency map by using the directional derivative feature of the target and incorporate it into the l
1-norm to enhance target and suppress background clutter simultaneously. Extensive experiments show the superiority of the proposed method to the other state-of-the-art methods in robustness and efficiency.
Gesture Recognition Method Based on Attention Mechanism for Complex Background
Authors: Jianchun Geng , Jiannuan Geng , Lili Wen 
Published: April 2021

Abstract
Human-computer interaction technology is still an unaccomplished goal due to complex background and changeable lighting conditions. A two-stage gesture recognition method is proposed to improve the accuracy of gesture recognition. The first stage combines attention mechanism and atrous spatial pyramid pooling (ASPP) which aims to segment gesture from complex background. The second stage uses a two stream convolutional neural network, combines features from the original picture and the output of the first stage, aims to realize the classification of gestures. It can recognize gestures more accurately compared with other gesture recognition methods.
End-to-End Encrypted Messaging Based on PGP with Forward Secrecy
Authors: Shen Yiming 
Published: April 2021

Abstract
In the era of big data, more and more users prefer messaging applications with end-to-end encryption to protect their privacy. However, if a hacker gets the password or encrypting key, it is possible to login into the account, or intercept the network packets and get the original messages. This paper introduces a new decentralized online messaging system that combines PGP and Diffie–Hellman key exchange and gives forward secrecy to end-to-end encrypted messaging. It is not possible for the hacker to decrypt the intercepted messages once the temporary session is ended, even if the hacker gets the PGP private keys.
A multi-point crosstalk attack detection and localization scheme based on hierarchical PCE in multi-domain optical networks
Authors: Qiwu Wu , Liu Xueyue , Jiang Lingzhi , Zheng Mingzhu 
Published: April 2021

Abstract
With the limited number of detection devices and the uncertainty of crosstalk attack source localization, how to propose an effective detection and localization scheme for multipoint crosstalk attack problem in multi-domain optical networks is an important problem that needs to be solved. A new multipoint crosstalk attack detection and location scheme HP-CADL (Hierarchical PCE-Crosstalk Attack Detection and Location) for multi-domain optical networks based on hierarchical PCE is proposed by using the monitoring point selection strategy of local immune algorithm and distributed localization considering continuous parametric analysis. VPI simulation results show that the scheme not only achieves low-cost monitoring and detection and accurate localization of multiple crosstalk attack sources, but also achieves higher accuracy and better performance than other similar schemes in the same multi-domain environment.
Research on QoS Centralized Routing of Software-Defined Aeronautic Swarm Network
Authors: Zhang Hao , Li HaiTao 
Published: April 2021

Abstract
To address the problem of unstable data transmission caused by frequent topology changes in Aeronautic Swarm Network (ASNET), a centralized routing algorithm for Quality of Service(QoS) of the aeronautic swarm network based on Software Defined Network(SDN) is studied. First, this paper designs a Software Defined Aeronautic Swarm Network (SD-ASNET) model. Then, taking advantage of the SDN controller’s ability to obtain global link-state information, we propose a Multiple Link-state Awareness routing (MLSA) based on the shortest path algorithm, taking into account link bandwidth, transmission delay and other factors. The simulation results show that the proposed centralized routing protocol makes full use of the centralized decision-making capability of the SDN controller, and effectively optimizes the link bandwidth utilization and transmission delay by using link parameter information.
Research On Visual Question Answering Based On Deep Stacked Attention Network
Authors: Xiaoqing Zhu , Junjun Han 
Published: April 2021

Abstract
Aiming at the problem that the existing visual question answering model has a language bias in high-level logical reasoning, the model describes images or answers questions with low accuracy. A visual question answering model based on deep stacked attention networks is proposed. At the same time, the attention mechanism is introduced to help the model fully mine the image information; secondly, the problem attention mechanism is introduced to pay attention to the image and the question information at the same time when the problem feature is extracted; finally, the features of the image and the question are integrated and the answer is selected in the classifier. Taking the Visual Genome and VQA2.0 data sets as examples for empirical analysis, the results show that the accuracy of predicting answers is 1.13% higher than the existing best model, which proves the effectiveness and applicability of the model.
Improved MOMVO algorithm to solve the multi-objective HFSP considering machine shutdown
Authors: Huang Hui , Luo Bin , Wang Zhongning , Yu Hao 
Published: April 2021

Abstract
Machine shutdown is a common phenomenon in production workshops, and it is also an important issue that many workshops should consider when scheduling. Based on the multivariety characteristics of hybrid flow shop scheduling problem(HFSP), this paper fully considers the machine shutdown constraint, establishes an HFSP model with dual goals of maximum completion time and machine load balance, and improves the multi-objective multi-verse optimizer(MOMVO) to calculate an example based on actual production data assumptions. And the research results showed that the scheduling scheme obtained by this model and algorithm meets the actual needs and can provide an effective scheduling scheme for the actual production of the enterprise.
A New Heuristic Reinforcement Learning for Container Relocation Problem
Authors: Jiang Tiecheng , Zeng Bo , Wang Yong , Yan Wei 
Published: April 2021

Abstract
Many research efforts on reinforcement learning (RL) is dedicated to solving challenging combinatorial optimization problems, including the container relocation problem (CRP) arising from maritime industry. In CRP, large and heavy containers are stacked on top of each other, those above the target container should be relocated elsewhere before retrieving it. This problem, which minimizes the number of relocations in the retrieval process, is NP-hard, while existing RL studies do not provide satisfactory results yet. We study the structure of CRP problem and design a new heuristic RL framework. On a set of commonly used examples, our RL demonstrates a performance almost equivalent to the most famous results.
Traffic flow prediction based on GRU-BP combined neural network
Authors: Zhang Xijun , Li Jiwen 
Published: April 2021

Abstract
With the rapid growth of transportation, all kinds of traffic data show explosive growth, and accurate and timely traffic forecasting is also particularly important. Among them, traffic flow prediction is the basis for realizing reasonable traffic guidance and control, and it is also a prerequisite for intelligent transportation. Traffic flow data belongs to a typical chaotic time series with strong non-linearity. In the application of neural network, a two-layer neural network can theoretically approach any continuous function infinitely, so as to achieve very accurate prediction. This paper proposes a predictive traffic flow model based on the combination of GRU network and BP neural network, and uses the US Twin Cities traffic data experiment to verify that the model is feasible in traffic flow prediction. The experimental results show that the combined model has high prediction accuracy and can capture the volatility of traffic flow at rush hour. At the same time, the model has strong robustness.
MultiAdapt: A Neural Network Adaptation For Pruning Filters Base on Multi-layers Group
Authors: Yang Jie , Xie Zhihong , Li Ping 
Published: April 2021

Abstract
Deep convolutional neural networks have been widely used in various AI applications. The most advanced neural networks are becoming deeper and wider, which has caused some large convolutional neural networks to exceed the size limit of the server or application. The pruning algorithm provides a way to reduce the size of the neural network while keeping the accuracy as high as possible. The automatic progressive pruning algorithm is one of the widely used pruning algorithms. The progressive pruning algorithm prunes a certain layer of the network in each iteration to reduce the sparsity while preserving the accuracy as much as possible. In this article, we design a new automatic progressive pruning algorithm named MultiAdapt. MultiAdapt combines the combination method and the greedy algorithm. This multi-layers progressive pruning method greatly increases the search space of the greedy algorithm, making it possible to obtain a better pruning network. We use MultiAdapt to prune large neural networks VGG-16 and ResNet. The experimental results show that the MultiAdapt algorithm is better than other mainstream methods in the balance of neural network model size and accuracy. For image classification tasks on the ImageNet dataset, our method achieved 88.72% and 90.55% TOP-5 accuracy on the 50% sparsity VGG-16 and ResNet, while obtaining nearly 2×reduction in parameters and floating point numbers. The operation is reduced, and the reduction is higher than the recent popular method.
Application of Artificial Neural Network(ANN) and Feature Selection Algorithm(FSA) on the ATLAS Experiment Data to Identify Higgs Boson
Authors: Tang Haozhan 
Published: April 2021

Abstract
From the 1950s, physicists started to smash particles together to temporarily form new smaller particles for observing and studying. With further development and enormous experiments, physicists had successfully built the Large Hadron Collider, which became the key hardware for proton colliding. The historical research and experiment in this field have provided us a number of referring data from the LHC experiment and the detailed information of the LHC upgrade to LH-LHC during the previous several years. The Higgs Boson detected by ATLAS Detector in 2013 was significant to scientific research, and its associated dataset could form a new hypothesis to predict the universe rule for small particles. In this study, both Artificial Neural Network(ANN) and Feature Selection Algorithm(FSA) are applied to the ATLAS experiment data to identify Higgs Boson, including the characteristics and fitness for the current model of nature. The study is based on the experiment result of head-on collisions of protons of extraordinarily high energy. The study is aimed to explore the potential of both methods of ANN and FSA to improve the significance of the experiment study and discovery. It will benefit further study to develop the full potential and the enormous scope of physics opportunities given by LHC.
K-means Clustering Algorithm and Its Improvement Research
Authors: Zhao YanPing , Zhou XiaoLai 
Published: April 2021

Abstract
Clustering is a typical unsupervised learning method, and it is also very important in natural language processing. K-means is one of the classical algorithms in clustering. In k-means algorithm, the processing mode of abnormal data and the similarity calculation method will affect the clustering division. Aiming at the defect of K-means, this paper proposes a new similarity calculation method, that is, a similarity calculation method based on weighted and Euclidean distance. Experiments show that the new algorithm is superior to k-means algorithm in efficiency, correctness and stability.
Detailed Description of Data Analysis Process of Internet of Things System
Authors: He XiaoPing 
Published: April 2021

Abstract
The purpose of data analysis of Internet of things system is to concentrate and extract the information hidden in a large number of seemingly disordered data, and find out the internal laws and useful value of the data object. In practical application, data analysis can help people make judgments and take appropriate actions. The data analysis of the Internet of things is a process of organizing and purposefully collecting data, processing data and analyzing data to form information. The analysis process is the support and execution process of the algorithm. In the data processing cycle, data analysis should be used properly in every link from data acquisition in the field perception layer to data transmission in the communication layer to data processing in the application layer, so as to improve the effectiveness and rationality of data processing.