bluuebunny/crossref_metadata_2025_split huggingface dataset:
Upgrade of the QA system in the BM@N experiment at the NICA
Authors: Driuk A V , Mashitsin K I , Merts S P , Nemnyugin S A , Roudnev V A , Stepanova M M 
Published: December 2020

Abstract
The BM@N (Baryonic Matter at the Nuclotron) is an experiment at the NICA (Nuclotron-based Ion Collider fAcility). The first physics runs were carried out with the collection of experimental data in 2018. For the physics analysis of events recorded by detectors of the BM@N the BmnRoot framework is used. It provides a powerful tools for detector performance studying, event simulation, data analysis and developing new algorithms that continually improve this environment. The QA (Quality Assurance) system is used to evaluate the quality of the event reconstruction procedures and collected data. It displays the results of comparing the properties of the simulated events with the reconstructed ones. However, previous version of QA system was nonflexible because its output was just a set of PNG images. Result of QA system modernization by using JSROOT library is described in current report. After this upgrade histograms are displayed in web browser. User can zoom histograms, change scales, get value of bin by moving the cursor on it, etc. It also became possible to get sample mean and standard deviation for each histogram. Upgraded system is quite scalable, so it can easily be extended with new histograms. At the moment, the modernized system is part of the BmnRoot framework.
Chemical freeze-out of light nuclei in high energy nuclear collisions and resolution of the hyper-triton chemical freeze-out puzzle
Authors: Bugaev K A , Vitiuk O V , Grinyuk B E , Yakovenko N S , Zherebtsova E S , Sagun V V , Ivanytskyi O I , Savchenko D O , Bravina L V , Blaschke D B , Farrar G R , Kabana S , Kuleshov S V , Nikonov E G , Taranenko A V , Zabrodin E E , Zinovjev G M 
Published: December 2020

Abstract
We present a summary of the recent results obtained with the novel hadron resonance gas model with the multicomponent hard-core repulsion which is extended to describe the mixtures of hadrons and light (anti-, hyper-)nuclei. A very accurate description is obtained for the hadronic and the light nuclei data measured by STAR at the collision energy

s

N
N

=
200

GeV

and by ALICE at

s

N
N

=
2.76

TeV

. The most striking result discussed here is that for the most probable chemical freeze-out scenario for the STAR energy the found parameters allow us to reproduce the values of the experimental ratios S
3 and

S
¯

3

without fitting.
Interaction between superconductors and weak gravitational field
Authors: Gallerati A 
Published: December 2020

Abstract
We consider the interaction between the Earth gravitational field and a superconductor in the fluctuation regime. Exploiting the weak field expansion formalism and using time dependent Ginzburg-Landau formulation, we show a possible short-time alteration of the gravitational field in the vicinity of the superconductor.
Impact of weak annihilation contribution on rare semileptonic B
+ → π
+
ℓ
+
ℓ
− decay
Authors: Ali Ahmed , Parkhomenko Alexander , Parnova Irina 
Published: December 2020

Abstract
In the Standard Model, b → s and b → d flavor-changing neutral currents (FCNCs) are not allowed at the tree level and are induced by loop effects. We consider the rare semileptonic B
+ → π
+
ℓ
+
ℓ
− decay, where ℓ = e, μ is a charged lepton. The dilepton invariant-mass spectrum and decay rate for B
+ → π
+
ℓ
+
ℓ
− are calculated in the effective Hamiltonian approach in two cases — by taking into account the weak annihilation diagrams and without this contribution. Our predictions for the branching fraction of the B
+ → π
+
ℓ
+
ℓ
− decay, being dependent on the choice of the B → π form factors, are in agreement with the existing LHCb results within experimental uncertainties. Moreover, accounting for the weak annihilation contributions allows us to get a better agreement with the experimental data on the entire q
2-distribution in the kinematically allowed region, in particular, in its lowest q
2-part. This provides an alternative description of the data than a previous analysis, in which the low-q
2 enhancement seen experimentally was obtained through the long-distance contributions from the light vector mesons.
Spin-parity assignments of excited

Ωb−

-baryons in the Quark-Diquark Model
Authors: Ali A , Dobrynina A , Oleinik E , Parkhomenko A 
Published: December 2020

Abstract
Recently the LHCb Collaboration have found four baryonic states called Ω
b
(6316), Ω
b
(6330), Ω
b
(6340), and Ω
b
(6350) in the

Ξ
b
0

K
−

invariant mass spectrum. Possible spin-parity assignments of these resonances in the Quark-Diquark Model as P-wave baryons are discussed. Parameters of the effective Hamiltonian are determined from the observed spectrum of

Ω
b
−

-baryons and compared with the ones extracted from the mass spectrum of

Ω
c
*

-baryons. Alternative interpretation, in which the three lowest-mass states have the spin-parities JP
= 3/2−, 3/2−, 5/2−, predicts an

Ω
b
−

-baryon with the mass M ≃ 6304 MeV which is not yet seen experimentally.
Optimal observables as a probe of CP violation in the

qq¯→Zγ→vv¯γ

process
Authors: Belyaev N L , Soldatov E Y 
Published: December 2020

Abstract
A possible CP violation effects in neutral currents are predicted in a wide class of theories Beyond the Standard Model (BSM). If such a violation will be discovered, it may shed light on the problem of the baryon asymmetry of the Universe. In this paper, an Effective Field Theory (EFT) approach is used to parameterize the BSM Zγ interaction. The optimal observables technique is applied to probe the CP-even and CP-odd anomalous EFT operator within the NTGC phenomenological model. Additional cut requirements on the photon transverse momentum pT
were considered in order to enhance the possible BSM signal.
What can the CNO neutrinos flux measurement done by Borexino say about 40K geoneutrino flux?
Authors: Sinev V , Bezrukov L , Karpikov I , Kurlovich A , Mezhokh A , Silaeva S , Zavarzina V 
Published: December 2020

Abstract
Borexino collaboration announced the observation of CNO neutrinos flux. Its value appeared larger than expected in case of the Sun high metallicity model. This could be regarded as evidence of large potassium content inside the Earth. The potassium abundance can reach (1.5 ± 1.0)% in the whole Earth and the Earth heat flux can be at the level of 200-300 TW. To resolve the problem a new experiment is demanded with a detector similar to Borexino one but better in backgrounds or a detector with another techniques of neutrino measurement, for example on base of 115In.
Status and Future of Neutrino Astronomy and the Global Neutrino Network
Authors: Spiering C 
Published: December 2020

Abstract
Attempts to build neutrino telescopes at the cubic kilometre scale date back to the 1970s. It took until 2010 when the first detector of this size, IceCube, started data taking. In 2013, IceCube has detected a diffuse flux of cosmic neutrinos, and in 2017 first evidence for an individual source has been obtained. In-depth exploration of the landscape of the high-energy neutrino universe requires even larger detectors, and it requires detectors on both hemispheres of the Earth. Two devices on the Northern hemisphere are currently under construction and started data taking with initial configurations. Further progress in the field calls for global coordination. The instrument to achieve worldwide cooperation and coordination is the Global Neutrino Network, GNN. This review includes a sketch of goals and achievements of GNN.
Intellectual property protection of big data
Authors: Lu Meng 
Published: December 2020

Abstract
With the rapid development of artificial intelligence technology, the application of big data becomes more and more popular in the 21st century. Through unremitting efforts, China has successfully joined the ranks of the big data countries and entered the big data era. However, while people enjoy the convenience of global big data, the problem of intellectual property protection of big data remains to be solved. Based on the analysis of the current situation, difficulties and causes of the intellectual property protection of big data, this paper puts forward the suggestion that the new big data should be an independent object of intellectual property within the framework of the existing intellectual property protection system, in order to provide better protection for big data.
Design of Gamified Tool to make workshops Effective
Authors: Xiong Zhiyong , Xie Jiawen 
Published: December 2020

Abstract
Few organizations have combined workshops with gamification to help to save costs, improve the enthusiasm and output efficiency of participants and reduce various deficiencies. According to the theory of Octalysis, this article proposes Experience-Externalize-Combine-Internalization (EECI model) gamification tool design strategy for organizing concept workshops, and takes a domestic appliance company in China as an example to use the tool in its concept workshop. According to the EECI model, the interaction among participants will promote the externalization of their ideas.
DS-Bit: Delay-Sensitive BitTorrent-Based Data Synchronization Protocol
Authors: Zhu Genwei , Wang Yongli 
Published: December 2020

Abstract
The main purpose of BitTorrent is to download files for individual users, and there are problems such as slow startup speed, insensitive to time delay, and slow synchronization of large-scale network data. This paper analyzes the bottleneck of BitTorrent’s technical framework, and proposes delay-sensitive BitTorrent-based data synchronization protocol (DS-Bit). With the Pre-Connect strategy, we significantly reduces the impact of the delay between nodes in the system on the data synchronization time by sending messages in advance, and the delay-considered TFT rule (DCTFT) that considers the delay optimizes the choice of nodes to send data. Because the transmission rate of DS-Bit is too fast, if it is used for file downloads for individual users, the node that completed the first will exit the system early, which will reduce the total download rate of the system. Therefore, DS-Bit is mainly used for data synchronization between distributed servers.
MixedCache: Enabling Flow Directed Rule-Caching Scheme based on Heterogeneous Cache for OpenFlow
Authors: Yang Jialun , Yang Yi , Liu Jiahao , Liu Rulin , Qiao Peng , Wang Baosheng 
Published: December 2020

Abstract
OpenFlow switches in SDN use Multiple Flow Tables (MFTs) for fine-grained flow control. Commodity switches integrate hardware storage resources such as SRAM and TCAM to store flow tables to achieve high-speed lookups. Many increased flow tables are rapidly exhausting these hardware storage resources, which makes the switches have to balance high-speed search and massive storage. The rule-caching scheme is a popular method to solve this problem, which caches the most commonly used rules into hardware storage resources. The existing rule-caching schemes are based on single hardware storage resources, and they cannot flexibly adjust the caching strategy according to the traffic characteristics. Simultaneously, the deployed commodity switches face the problem of difficulty in changing the size of SRAM and TCAM. This paper innovatively proposes the MixedCache scheme, which makes full use of the hardware storage resources in the switch according to the skewed characteristics of network traffic. MixedCache stores the large flows in SRAM by exact match and stores the small flows in the TCAM by wildcard match. MixedCache does not need to change the size of the deployed switch hardware storage resources, but makes full use of existing resources. Compared with the rule-caching scheme based on the exact match, the cache hit rate can increase by up to 15.61%. Compared with the rule-caching scheme based on the wildcard match, the cache hit rate can increase by up to 29.69%.
Study on multimodal network expansion for hazardous materials based on disruption
Authors: Yu Lutong , Song Liying 
Published: December 2020

Abstract
This paper studies the multimodal network expansion problem for hazardous materials. In this study, the transportation characteristics of hazardous materials and disruption are considered, the mixed integer probability robust model is established with risk and cost as the objective. A numerical case is solved by CPLEX, and the decision of network expansion and flow distribution is obtained. Through the comparison between the robust model and the traditional model, it is proved that the robust model can complete the transport of hazardous materials in the network with lower cost-risk. The paper also provides advice to decision-makers with different risk preferences through the Pareto frontier.
Research on the Influence of Online Learning on Students’ Desire to Learn
Authors: Shi Gengwei 
Published: December 2020

Abstract
Due to the coronavirus pandemic, many students are learning online. Because of the particularities of the online environment, the different study habits of each student and the different teaching characteristics of instructors, the evaluation of online learning differs widely. This paper uses questionnaire to examine the situation of college students’ online learning in China and abroad and the factors influencing their desire for online learning. The study found that the main factors affecting students’ learning desire in the process of online learning are students’ self-learning abilities, teachers’ ICT literacy and hardware equipment conditions.
Key node mining algorithm for directed weighted air quality network based on propagation characteristics
Authors: Song Chen , Zhang Xiankun , Liu Xinqian , Ren Dawei , Dong Mei 
Published: December 2020

Abstract
The decline of air quality seriously affects human life and ecological environment. The blind allocation of governance resources leads to poor improvement. In order to allocate resources reasonably and improve treatment efficiency, a new key nodes mining algorithm for air quality system based on network structure and the characteristics of pollutant transmission is proposed, aiming at resource investing guidance. Firstly, the air quality network is established and its structural characteristics are analyzed. Secondly, according to the diffusion and attenuation mechanism of air pollutants in the network, a bidirectional transmission key node mining algorithm is proposed which takes both the in-links and out-links into consideration. Thirdly, a dynamic independent threshold propagation model in directed weighted network is proposed, and the number of activated nodes is used as evaluation criterion for key node mining results. Finally, experiment is executed on Jing-Jin-Ji PM2.5 air quality network. Experiment results show that the bidirectional transmission key node mining algorithm can get accurate results and good applicability in air quality network.
A Coherence Analysis Model for English Essay Based on Sentence Semantic Graph
Authors: Huang Guimin , Tang Hui , Wang Jiahao , Ye Xuan 
Published: December 2020

Abstract
Aiming at the problem that cannot be solved by the entity-based graph model, we propose a new discourse coherence quality analysis model (sentence semantic graph) by merging entity graph and semantic similarity graph of the text. Firstly, we use an improved coreference resolution module to process the coreference phenomenon in the text, and build a new type of entity graph with the processed results; secondly, we merge the entity graph and the semantic similarity based on the semantic space and get our sentence semantic graph lastly, we analyze the average outdegree of sentence semantic graph to indicate the degree of text coherence. Experiments show that this model is superior to the entity-based graph model and has a good effect on the automatic evaluation of English essay.
Sentiment Analysis Of Movie Reviews Based On Improved Word2vec And Ensemble Learning
Authors: bao Xiaoan , Lin Shasha , Zhang Ruilin , Yu Zechuan , Zhang Na 
Published: December 2020

Abstract
Taking the field of movie review as an example, this paper proposes a sentiment analysis method based on improved word2vec and ensemble learning. The basic design idea is: firstly build the corresponding corpus through new word discovery and use the TF-IDF algorithm to exponentially weight the word2vec word vector, which is used to integrate the semantic relationship between words and the importance of vocabulary information into the model; secondly, to avoid the cumbersome problems of data labeling, the existing algorithms of automatic labeling reviews are improved to increase the adoption rate of data; finally, Stacking algorithm is used to train and classify the emotional data. The proposed model can simplify the domain text representation and improve the classification performance of the model. The experimental results show that compared with existing methods, the accuracy, precision and recall rate of the algorithm proposed in this paper have been improved on film review data.
An implementation of SM4 algorithm based on mask
Authors: Xinchao Li 
Published: December 2020

Abstract
Aiming at the problem of SM4 algorithm resisting power attack, this paper studies the implementation structure and S-box characteristics of SM4 algorithm, constructs the S-box realized by multiplication mask, and designs a semi random mask circuit implementation scheme of SM4 algorithm. In this scheme, the middle values of the first six rounds and the last six rounds of SM4 algorithm are completely random masked, and the intermediate values of the middle 24 rounds are fixed mask, which reduces the consumption of random masks. After security analysis, the scheme can resist the first-order power consumption attack, and has less random number consumption and lower implementation area.
A novel method of extracting geometric features of ships based on GrabCut algorithm
Authors: Zha Jiale , Chen Huaixin , Ren Chengjie , Wang Chenggang , Li Siqi 
Published: December 2020

Abstract
Using remote sensing images to accurately identify ships is of great significance in both military reconnaissance and civil surveillance. This paper presents a method of extracting geometric features of ships based on GrabCut image segmentation. Firstly, by establishing gaussian mixture model of image area, the problem of segmentation of suspected target area is transformed into graph minimum cut, then the suspected target is segmented by iteration energy minimization, and the moment method is used to extract the minimum enclosing rectangle of ship target, so as to obtain the geometric features of ship target. Experimental data analysis shows that compared with the traditional algorithms, the proposed method can quickly extract the geometric features of ships in different directions in remote sensing images, and has strong robustness, which can meet the requirements of target detection engineering.
Predict New App Quality By Using Machine Learning
Authors: Zhong Zhenghuan , Bao Yuhang , Shen Shiyao , Zhou Entong 
Published: December 2020

Abstract
Increasingly, individuals and companies are developing applications and selling them online. Competition between different companies has led to tens of thousands of applications being put on the market for users to choose from. There are numerous functions on the market to give app evaluation. For users, they do not need to contemplate how to select an app for a long time, because user comments and ratings can give them a clear instruction for user to select an app they need from abundant apps. As a result, we would like to explore the possibility. Hence, we think that we can make a prediction function for the prospect of the app market of whether we can make a prediction function for the app market, akin to a weather forecast or stock market forecast. Our group not only wants to know what the future of the app market is, but also want to find out why some high-quality apps are not getting the downloads they should. With the development of AR and VR technology, the future application will combine with these technology to bring user a immersive experience. In addition, high quality application without AR or VR are less competitive. We plan to answer these questions by using machine learning techniques.
A method of positioning and recognition of electronic scale characters based on deep learning
Authors: Xu Zhengguang , Wang Lutian , Niu Siqiang , Kan Guojin 
Published: December 2020

Abstract
Digital characters are widely used in fields such as industry, equipment display and detection. According to the application background of reading recognition of electronic scale in chemical experiment examination of middle school, in order to improve the application of artificial intelligence in the education field, meet the requirements of intelligent examinations, and save manpower, a character positioning and recognition method based on the combination of deep learning and traditional image processing is proposed, which can automatically and quickly identify the reading of digital tube display screen of electronic scale, and according to the test requirements and test sites can automatically determine whether the students’ weighing operation is correct. Firstly, the yolov3 target detection algorithm and deeplabv3plus semantic segmentation algorithm are used to locate the character area of the display screen, and then a single digital tube character is obtained by projection segmentation. Finally, the recognition algorithm based on support vector machine is used to recognize characters. The experimental results show that the method has a high accuracy of positioning and recognition, and can adapt to different lighting, digital tube size and character tilt angle, has certain anti-interference ability, meets the real-time requirements, and has high application value.
Research on Speech Synthesis Technology Based on Rhythm Embedding
Authors: Wu Tianxin , Zhao Lasheng , Zhang Qiang 
Published: December 2020

Abstract
In recent years, Text-To-Speech (TTS) technology has developed rapidly. People have also been paying more attention to how to narrow the gap between synthetic speech and real speech, hoping that synthesized speech can be integrated with real rhythm. A rhythmic feature embedding method for Text-To-Speech was proposed in this thesis based on Tacotron2 model, which has arisen in the field of TTS in recent years. Firstly, rhythmic feature extraction through World vocoder can reduce redundant information in rhythmic features. Then, rhythmic feature fusion based on Variational Auto-Encoder (VAE) network can enhance rhythmic information. Experiments are carried out on the data set LJSpeech-1.0, and then subjective evaluation and objective evaluation are carried out on the synthesized speech respectively. Compared with the comparative literature, the subjective blind hearing test (ABX) score increased by 25%. At that same time, the objective Mel Cepstral Distortion value (MCD) declined to 12.77.
Multi-Camera Panoramic Imaging System Based on Adaptive Brightness Uniformization For The Truck
Authors: Cheng Bang , Sun Tongyi , Cheng Jianghua , Liu Tong 
Published: December 2020

Abstract
With the development of science technology and logistics industry, the transportation of large trucks is also indispensable. It is also accompanied by many traffic accidents. The paper mainly focuses on the problem of blind spots in the parking of large trucks. A panoramic imaging system with six cameras is implemented to improve its safety performance. It mainly includes the realization of modules included simultaneous acquisition of images by multiple cameras, image calibration and correction, top view image generation, image stitching and image fusion. In view of the brightness difference of images acquired by different cameras, we proposed an adaptive brightness uniformization method based on γ-transformation. Meanwhile, we compared the proposed method with various image enhancement methods included histogram equalization, Laplace operator and logarithm transformation. The experimental results in the system demonstrate the superiority of our proposed method compared to others.