bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on sentiment classification of micro-blog short text based on topic clustering
Authors: Tu Shifen , Yang Bo 
Published: March 2021

Abstract
Aiming at the shortage of research on micro-blog short text fine-grained sentiment classification, a fine-grained sentiment classification method about micro-blog short text based on PLSA model and K-means clustering model was proposed. PLSA is used to calculate the probability matrix between documents and topics, words and topics in the corpus. In terms of the probability distribution of words and topics, K-means algorithm is used to cluster the probability distribution of words on topics and merge the similar topics. Based on the sentiment ontology library, emotion recognition is carried out for the merged topics. Then, according to the merged document and topic probability matrix, the document sentiment category is classified. The experimental results show that the sentiment analysis method integrated with PLSA and K-means can obtain higher classification accuracy than the PLSA model method alone.
Quantum Max-min Ant Colony Algorithm For QoS Routing Problem
Authors: Li Yalong , Wei Wang , Jing Sun , Jie Wan 
Published: March 2021

Abstract
Aiming at the ant colony algorithm is weak to solve the problem of QoS routing, this paper proposes a routing algorithm that combines quantum computing with the max-min ant colony algorithm. The algorithm introduces a quantum selection strategy, uses qubit probability amplitude to encode the path, and dynamically adjusts the quantum rotation gate to update the path quantum coding on the basis of the minimum cost to meet the constraints and other optimal conditions, combined with the maximum and minimum pheromone determination mechanism to find the optimal path, effectively control the evolution speed and direction, and accelerate the algorithm convergence. Experimental results show that the algorithm is effective in controlling the speed and direction of evolution, and has better optimization ability and convergence performance than ant colony algorithm.
An Algorithm for Detecting the Integrity of Outer Frame Protection Net on Construction Site Based on Improved SSD
Authors: Fan Yu , Chen Xianqiao , Xie Jinguang , Fu Zhenjie 
Published: March 2021

Abstract
In recent years, the continuous development of the basic construction industry has promoted the progress of urban social economy, and the safety issues in construction have attracted more and more attention. The integrity of the external protection net is of great significance to the safety of the construction personnel. Most of the traditional methods of detecting damage to the protection net have problems such as poor real-time performance and low detection accuracy. In this paper, we present a novel approach based on SSD for solving the problem of the integrity detection of the external frame protection net in the construction site. The improved network merges adjacent feature layers to enrich the semantics of shallow feature maps, thereby improving the detection accuracy of the network, and simultaneously introduces a shallower feature layer and a more advanced receptive field module to improve the detection ability of small targets. The experimental results show that compared with the SSD algorithm, the improved algorithm has higher detection accuracy in the detection task of the external protective net on the construction site, and can meet the real-time detection requirements.
BiGRU-Multi-Head Self-Attention Network for Chinese sentiment classification
Authors: Wang Shitao , Li Jiangfeng , Hu Defeng 
Published: March 2021

Abstract
The rapid development of Internet and the extensive user base have led to a significant increase in the propagation speed of information and public opinion. Quick and accurate sentiment analysis of public opinion can help analyze and manage public opinion. Since BiGRU can simplify network parameters, and the Multi-Head Self-Attention(MHSA) mechanism can learn long-distance features more accurately and quickly. In this study, we propose a BiGRU-MHSA network to perform Chinese sentiment classification. The innovation of the model lies in the combination of BiGRU and MHSA in order to focus on several semantic centers and comprehend the whole text more precisely and efficiently. At the same time, we use the Chinese comment datasets on Weibo to verify the effectiveness and accuracy of the model. From the experimental results, we find that our proposed model improves the accuracy by about 1% on different comments datasets without consuming too much computing resources.
Intelligent classification of breast cancer based on deep learning
Authors: Liao Yuhang , Peng Yulan , Liu Dongquan , Liu JingYan 
Published: March 2021

Abstract
How to use artificial intelligence to assist physicians in analyzing complex breast cancer medical data has become an urgent problem to be solved. From the perspective of auxiliary diagnosis, we propose a method to classify breast cancer grades by cases. Based on the Chinese electronic medical records of breast cancer provided by the Department of Ultrasound at West China Hospital, we compared neural network methods, including FASTTEXT, TEXTCNN, TEXTRNN. And proposed the N-LM-ATT model. N represents the n-gram feature, LM represents the Long Short-Term Memory (LSTM) network, and ATT represents the Attention Mechanism (AM). This article focuses on the classification of Chinese breast cancer text data based on natural language processing (NLP) method. By comparing the above methods, the N-LM-ATT model achieved the best performance on the breast cancer ultrasound dataset of West China Hospital.
Multi-task Learning Network based on Attention for Aspect-Based Sentiment Analysis
Authors: Dong Yang , Wang Jing , Wang Junwei 
Published: March 2021

Abstract
Aspect-based sentiment analysis is to analyse the sentiment polarity of the aspect in a specific context. The previous methods mostly use the working mode of pipeline model, and internally use Recurrent Neural Network to predict the sentiment polarity of the aspect. This way clearly defines the order of aspect extraction and aspect-level sentiment classification, which will lead to the problem of error transmission. And traditional models have great limitations. In this paper, we propose a multi-task learning network based on attention (MNA), which uses shared word embedding to transmit to downstream tasks, allowing extraction and classification tasks to be processed simultaneously. We design a network architecture based on attention mechanism, and use the improved multi-head attention mechanism to transfer information from extracted tasks to classified tasks. Experimental results on three benchmark datasets show the effectiveness of MNA model.
Performance Comparison of Android Malware Detection Methods
Authors: Wang Yuandi 
Published: March 2021

Abstract
With the growth of Android apps market share, there are more and more kinds of malicious apps for Android system. At the same time, the method for malware detecting is evolving. In order to deal with the growing threat of malware, many detecting methods are combined with machine learning algorithms. In this paper, we compared the ability to detect malware of five algorithms on a dataset about Android apps networking. The result revealed that the networking data could be used as a reference for the classification of Android apps. Moreover, among the five algorithms used in this paper, the ensemble learning algorithm using the decision tree as base leaner performed the best in the comparison. It reached more than 75% accuracy when predicting malware using the test set.
Road Information Detection Method Based on Deep Learning
Authors: Zheng Shangsheng , Zhang Jiangzhou , Che Xiaobo , Li Yanqiang 
Published: March 2021

Abstract
Autonomous driving technology has developed rapidly in recent years, and computer vision has shown significant role in automated vehicles. Researchers use the combination of computer vision and deep learning to improve the speed and accuracy of road element detection technology. This article focuses on the semantic segmentation and road detection elements, especially in lane line segmentation and signal light detection. It summarizes the visual inspection technology and a series of visual perception algorithms based on deep learning. Although semantic segmentation and target detection technologies in general scenarios are already very mature, they are not ideal when applied to autonomous driving environments, especially where segmentation and detection are performed simultaneously. Therefore, this paper builds a fusion network of lane line segmentation and signal light detection suitable for autonomous driving scenarios.
Research on improving LIO-SAM based on Intensity Scan Context
Authors: Wang Chao , Zhang Guobao , Zhang Ming 
Published: March 2021

Abstract
During indoor mobile mapping, localization plays an important role. It is difficult for IMU, odometer, Lidar or SLAM algorithm singly to meet the high efficiency, real-time, accurate and robust performance. So the fusion of the data measured by these different methods is a current research hotspot. However, most of the researches still focus on the loosely coupled fusion based on filtering methods, and the data cannot be fully utilized. In this paper, based on LOM-SAM framework, the Intensity Scan Context is introduced to extract keyframes and detect loop closure which will provide a closed-loop factor, together with IMU pre-integration factor and Lidar odometry factor to construct the factor graph. Then incremental smoothing optimization algorithm is used to get high precision trajectory, realize the tightly-coupled Lidar and IMU positioning. The results show that the number of keyframes is reduced, the elevation error is effectively decreased, and the real-time performance is improved without decreasing the accuracy of LIO-SAM.
Recognition of Multi-Intersecting Features of Boundary Representation CAD Model
Authors: Zhu Jiang , Xie Da , Tanaka Tomohisa 
Published: March 2021

Abstract
This research aims to solve technical problem of automatic feature recognition in a Computer Aided Process Planning system. This feature recognition method was developed to represent a 3D model as classes of manufacturing features based on graph theory. In addition, “accessibility” was proposed to recognize intersecting features with complicated graph structure which cannot be predefined by users. The experimental result shown in case study proofed that this proposed method is effective in multi-intersecting feature recognition.
Port Energy Level-Evaluation Research on the Coordinated Development of Hinterland Economy-Taking Chongqing Port as an Example
Authors: Deng Qiwei , Yu Haihong 
Published: March 2021

Abstract
With economic development, ports assume more responsibilities. However, the level of coordination between ports and hinterlands in my country’s inland river ports needs to be improved. By constructing a comprehensive evaluation system of port energy level-hinterland economic system, entropy method and coupling coordination Degree model for coordination analysis. The article takes Chongqing Port as an example to study the characteristics of coordinated development between port energy level and hinterland economy from 2010 to 2019. The results found that: 1) The coordination evaluation model effectively reflects the development status of Chongqing Port in the past 10 years; 2) With the increase of time, the port energy level and hinterland economic level of Chongqing Port are in a relatively stable state of improvement; 3) Port energy The degree of coordinated development between the economic system of the Hinterland and the hinterland shows a steady upward trend overall, but there is still room for improvement.
Heavy ion collider NICA at JINR
Authors: Kostromin S. , Sidorin A. 
Published: March 2021

Abstract
Main scientific goal of the NICA/MPD project
(Nuclotron-based Ion Collider fAcility/ Multi Purpose Detector)
under construction at JINR (Dubna) is exploration of the phase
diagram of strongly interacting matter in the region of high
compression. The planned experimental program includes studies of
possible signs of the phase transitions and critical phenomena in
heavy ion (up to Au) collisions at the center-of-mass energies up to
11 GeV/u. The collider experiment provides optimum conditions for
efficient energy scan measurements. Attainment of the required
average luminosity of the order of 1027 cm-2·s-1
for such physics experiment faces several accelerator physics and
technology challenges. Contrary to high energy colliders, the
luminosity of NICA case will be mostly limited by the Laslett tune
shift, while the beam-beam tune shift parameter will be negligibly
small. Flexible operational procedures for the beam storage and
short bunch formation have been developed to provide maximum peak
luminosity over wide energy range. Beam cooling is critical to
counteract luminosity decay due to intra-beam scattering. NICA
collider design addresses these issues and satisfies all the
requirements for the colliding beams experiments.
Making farming more sustainable by helping farmers to decide rather than telling them what to do
Authors: Kröbel R , Stephens E C , Gorzelak M A , Thivierge M-N , Akhter F , Nyiraneza J , Singer S D , Geddes C M , Glenn A J , Devillers N , Alemu A W , St. Luce M , Giardetti D 
Published: March 2021

Abstract
In Canada, the agricultural sector has long held a prominent economic, social and cultural position, from substantial evidence of extensive fishing and farming since the times of the first human settlements, to currently accounting for over 100 billion dollars of production and employing 2.3 million people. Steady growth in agricultural production in the country over several decades, supported by strong investment in public agricultural science, has allowed an increasing supply of a wide variety of food and agricultural goods to be available both within the country as well as allowing for substantial exports abroad and deep integration of the Canadian agricultural sector into global markets. Along with securing continued productivity growth in agricultural output for the future, policy makers and public sector agricultural scientists in Canada have become increasingly concerned with managing environmental externalities associated with agricultural production in order to achieve the objective of sustainable intensification of the sector. However, the process of identification of the best tools and practices to improve the sustainability of the agricultural sector in Canada has evolved over time due to shifting research priorities and dynamic changes in the problems facing the sector. In this paper we discuss applied and direct-to-farmer agricultural science research initiatives that are focused on identification and implementation of best environmental management practices at the farm level. We believe that involving farmers directly in scientific research and communication of scientific results provides for a deeper understanding of agro-environmental externalities. It also allows farmers to find greater adoption potential in their specific farm system, thus combining both environmental and economic sustainability. We trace the history of public agricultural science engagement with Canadian farmers to address economic and environmental problems in the sector. We then provide examples of successful public sector projects based in applied agricultural science research that foster effective farmer/scientist collaboration, leading to improved agriculture sustainability in Canada.
Does tall vegetation warm or cool the ground surface? Constraining the ground thermal impacts of upright vegetation in northern environments
Authors: Way R G , Lapalme C M 
Published: March 2021

Abstract
Increased upright vegetation growth (i.e. trees and shrubs) in northern environments can profoundly impact ground surface thermal conditions through winter warming (e.g. enhanced snow trapping) and summer cooling (e.g. increased shading). The debate over these opposite effects emphasizes the need to better constrain net temperature impacts of upright vegetation on soils in northern environments. We generate a series of simulations with a widely-used permafrost model to partition the absolute warming and cooling impacts of upright vegetation on ground surface temperatures for a variety of shading scenarios, climates and surficial materials types (i.e. bedrock, mineral and organic soils). These scenarios simulate annual temperature differences between the air and ground surface caused by upright vegetation to provide likely ranges for the net effects induced by vegetation. These simulations showed that ground surface temperature warming in the winter mostly overwhelmed ground surface cooling in the thawing season even when simulations included extreme shading effects. Constraining the simulations to current best estimates of the possible summer cooling impact of vegetation yielded a dominant winter warming signal for most snow depths and climate types. Differences in the magnitude of air-surface temperature offsets between sites underlain by bedrock, mineral and organic soil highlights the importance of considering differences in unfrozen moisture content in areas where the ground freezes and thaws seasonally. The results of this study suggest that the net ground surface temperature impacts of increased snow trapping by vegetation will far exceed cooling caused by enhanced shading following increases in tall vegetation in most northern environments.
Management model in digital ecosystems
Authors: Burlov V , Grachev M 
Published: March 2021

Abstract
Rapid pace of digitalization of ecosystems in the modern world affect social and economic systems. State-of-the-art web technologies, a huge information system, have an increasing impact on human life. The information environment undergoes adverse effects during its functioning. To maintain functioning of the system information component, a properly constructed system and an adequate model of response to external effects are required. The response model in digital ecosystems will be designed with regard to three processes, namely: the problem formation process, the problem identification process, and the problem neutralization (elimination) process. An indicator of the efficiency of ecosystem functioning will be identification and neutralization of each problem. Simulation results confirmed the main trends in digital ecosystems functioning. The development and subsequent application of methods and techniques to achieve the set goal in a shorter time will always be relevant and in demand. To implement a rational approach to making managerial decisions, the leader responsible for digital ecosystem functioning can use network planning as a plan for the entire cycle of processes for maintaining the information security of the ecosystem as a whole.
Financial ecosystems: analysis and development paths
Authors: Evdokimova Y 
Published: March 2021

Abstract
This research aims to identify the role of ecosystems in the modern financial world. Banks are most actively involved in the formation of ecosystems, concentrating various offers around their traditional areas of activity. The study of ecosystem strategies used by banks revealed the areas that banks consider as the main ones when forming their own ecosystems. The reasons that caused an active interest in the formation of ecosystems by banks are studied. The analysis of the leading financial ecosystems of countries, including the Russian Federation, is made. The main models of banking ecosystems on the market and their features are shown. Factors that have a stimulating effect on the development of ecosystems are identified. Based on this information, General summary conclusions are formulated.
Opportunities for universities to use the German experience in the startup ecosystem development
Authors: Dorofeeva V V 
Published: March 2021

Abstract
The paper analyzes modern theoretical concepts of the startup ecosystem. The subjects of the startup ecosystem are studied, and their main functions are presented. The current state of innovation ecosystems in German universities is analyzed, and the prospects for their development are determined. The essence of the main components of university innovation ecosystems is revealed, and the principles of their formation and implementation are determined. The features of the innovation ecosystem development in German universities are investigated. The directions of the innovation ecosystem formation in Russian universities are shaped.
Energetic and ecological justification of RE-hybrid systems for vulnerable ecosystems
Authors: Elistratov V V , Denisov R S 
Published: March 2021

Abstract
About two-thirds of the territory of Russia, which covers 11 million km2, lies in a region with severe climatic conditions. These northern ecosystems have a unique climate and are supplied with supplies by outdated diesel generation. Therefore, an urgent need has arisen to modernize the power supply systems in question using modern energy-efficient technologies, including power plants based on Arctic renewable energy sources that convert the high natural potential of renewable energy on these territories. For example, the coastal territories of the Russian North are situated in the zone of high wind power potential. In the article a method of modernization through the use of modular hybrid energy complexes are proposed. In total, about 4,700 such stations can be installed in Russia, and the total environmental effect from them will be about $1 million. Moreover, these power plants have a high science intensity. The problem of correct optimization of the parameters and architecture of wind-diesel power plants is also investigated in the article.
Power supply of the center for development of gifted children in Kaliningrad region based on renewable energy sources
Authors: Beley V F , Zadorozhnyy A O , Kiryukhin A A , Yurkov S V 
Published: March 2021

Abstract
One of the modern vectors of world energy development is the transition to an energy self-sufficient principle of functioning of geographically limited local facilities. The paper presents an analysis of the possibility of the transition and the concept of using renewable energy sources in the transition to an autonomous mode of electricity and heat supply of the Center for the Development of Gifted Children in Kaliningrad Region.