bluuebunny/crossref_metadata_2025_split huggingface dataset:
Garbage detection and classification based on improved YOLOV4
Authors: Guo Dongwei , Cheng Lufei , Zhang Meng , Sun Yingying 
Published: September 2021

Abstract
Object detection is an important research task in computer vision. With environment problems becoming seriously, garbage detection has become a hot direction in object detection. Though great progress has been made in garbage detection, there still exists challenges for general-purpose detectors with no public datasets and low recognition accuracy rate. Based on YOLOV4 model, this paper proposes an improved YOLOV4 detector for garbage detection. Specifically, CBAM is added to the feature extraction network in order to better extract features in deep networks, and focal function is integrated into the loss function to improve class imbalance. When preparing the data, environment information and random information are added to pictures to simulate the distribution of garbage in real environment, and finally an urban household garbage dataset containing 47 classes of 45,910 images named TrashSet has been produced. Experiments results on TrashSet verify that our detector has a considerable performance, and mAP reaches 97.15%.
Multi-source image target detection technology based on salient feature fusion
Authors: Cui Yuyong , Wang Shengzhe , Gao Xinyi , Wu Zhongjian , Yong Yang 
Published: September 2021

Abstract
The ground target can be easily affected by the complexity of ground objects and photoelectric interference, so the accuracy of target detection in complex background is not always satisfying. To this end, joint target detection of Infrared/Visible image, IR/VI, is adopted, and the feature representation is achieved by applying the method of deep image feature extraction. According to the different imaging characteristics of the target, the feature fusion is carried out to form a unified target feature representation. As a result, target detection based on deep learning can be achieved by the model established by such fused feature. Experiments show that the proposed method can effectively improve the accuracy of target detection.
MDCount: A Lightweight Encoder-Decoder Architecture for Resource-Saving Crowd Counting
Authors: Meng Xiaolong , Ren Zhengfei 
Published: September 2021

Abstract
This work put forwards a lightweight encoder-decoder architecture MDCount designed unambiguously for real-time high-precision crowd counting with constricted calculation resources. The lightweight backbone network MobileNetV2 is tailored to decrease parameter numbers in an acceptable accuracy. The encoder-decoder architecture with atrous spatial pyramid pooling modules is proposed to recover the spatial and contextual information at multiple scales. Experiments on realistic and challenging datasets and comparing contemporary approaches demonstrate that our method MDCount accomplishes equivalent performance with smaller computation costs.
SMCN: Simplified mini-column network for visual place recognition
Authors: Huang Guanyu , Chen An , Gao Hongxia , Yang Puguang 
Published: September 2021

Abstract
Visual place recognition(VPR) is challenging because the places to be recognized are often affected by complex environmental changes. It is important for loop closure detection in SLAM. In recent years, a large range of approaches have been developed to address this challenge. Among these approaches, the use of biological heuristics and structural information have improved the VPR performance. In this paper, we attempt to improve the biological heuristic method MCN, and combine the available but often overlooked structural information intra-set similarity to propose our first algorithm SMCN. Furthermore, we propose a novel temporal filter that considers temporal continuity and combines it with SMCN to get our second algorithm SMCNTF. Evaluation of both algorithms on ten dataset combinations shows that, our best model SMCNTF has a maximum increase of 32% in average precision and at least a 100-fold increase in computing efficiency. Moreover, fewer parameters need to be tuned comparing with MCN. Generally, our algorithm is much better than MCN.
A Dynamic Method Based-matrix for Batch Updating Decision Regions of Hybrid Information System
Authors: Chen Jiajun , Yu Shuhao , Jin Ping , Ma Yan 
Published: September 2021

Abstract
Dynamics and complexity are the important characteristics of current information systems, and intelligent decision-making problem of complex information systems in dynamic environment is an important research content in the fields of intelligent decision-making. Decision-theoretic rough set model is an important model based on rough computing, and matrix is an effective mathematical tool, which was widely used in various fields of intelligent computing because of its easy representation and convenient calculation. In this paper, neighbourhood decision-theoretic rough sets(NDTRS) and the dynamic updating methods of decision regions of hybrid decision information systems were studied, and a dynamic method based-matrix for batch updating decision regions of hybrid decision information systems was proposed by introducing neighbourhood relation matrix and probability matrix to calculate the decision regions.At the same time, In the process of algorithm design, the incremental matrix is defined to updated the probability matrix, so as to improve the algorithm from the perspective of time complexity.Furthermore,the algorithms were described and analyzed in detail.Finally, the experimental results fully proved the effectiveness and feasibility of the algorithm.
Earthquake observation data grading and storage research
Authors: Zhu Kun , Liu Hui , Ji Yu , Li Zehao 
Published: September 2021

Abstract
With the constant development of contemporary science and technology and increasing popularization of the big data technology, the concept of “database” has been integrated into people’s daily work and life. The database system also plays an indispensable role in earthquake early reports. This paper briefly analyzes introducing the mature big data technology to earthquake observation and seeks graded storage of databases to ensure a faster and more accurate retrieval of data as well as timely reports of earthquakes.
Research and Optimization of Conflict Search Algorithm for Multi-Agent Path Planning Based on Incremental Heuristic
Authors: Li Yang , Wang Jun , Zhang Hualiang 
Published: September 2021

Abstract
Conflict Based Search(CBS) is used for multi-agent Pathfinding (MAPF) to enable each Agent to reach the target node. The CBS algorithm uses the heuristic algorithm A* search to calculate the MAPF solution, and the path planning uses forward search, which cannot explore the path of the unknown region. On this basis, this paper proposes that, in the case of unknown map and changing environment at any time, when encountering new obstacles, the information obtained from previous search should be used without completely replanning the path. Because of the idea of incremental programming, the number of reprogramming times and the number of affected nodes can be reduced. The optimized algorithm consumes less time and memory, and improves the efficiency of path planning.
A stacking-based classification approach to android malware using host-level encrypted traffic
Authors: Xue Zhixing , Niu Weina , Ren Xixuan , Li Jie , Zhang Xiaosong , Chen Ruidong 
Published: September 2021

Abstract
In recent years, smartphones have been developing fast. Android, a mobile platform convenient and open to all, has attracted more audience than any one of its counterpart. However, mobile devices are frequently attacked by malware, which calls to malware detection. Currently, we are lacking studies of Android malware detection based on ensemble learning. In this work, we propose a model to detect Android malware. The model takes the encrypted traffic that the malware generates as input. Through clustering, the model removes the third-party traffic and retains the purity of the first-party traffic. The model extracts traffic features to construct host-level traffic fingerprint and classifies the malware through stacking-based ensemble learning. We use the publicly available dataset CICAndMal2017 to build the classification model. This dataset successfully classifies malware into different categories. In the controlled experiments we use SVM and Random Forest models. The results show that our model is significantly more accurate in classifying malware than SVM and Random Forest models, with an accurate rate of 96.7% in the optimal condition.
Lithology recognition of complex carbonatite by deep learning
Authors: Li Luoning , Qu Honglve , Jiang Yanan , Zhang Kai , Kang Lei 
Published: September 2021

Abstract
The formation of carbonate reservoirs is affected by many factors such as sedimentation, diagenesis, and tectonic evolution. The rock composition and pore structures are complex, which brings challenges to the interpretation of reservoir lithology. Therefore, a novel approach of one-dimensional convolutional neural network architecture (1DCNN) based on the optimization of gradient descent algorithm for lithology identification is proposed. By fully combining logging physical parameters and vertical structure sequence context information, the deeper intrinsic rules between reservoir lithology and wireline logs can be found. With feature extracting of multi-scale deep feature extraction from multivariable wireline logs, the ability of wireline logs to express lithological features is improved, and the accuracy of lithological identification is further improved. In order to illustrate the prediction effect, the carbonate reservoir of the Majiagou Formation in Block 41-33 in Sulige Gas Field is taken as an example. The results reveal that 1DCNN improves the accuracy of carbonate lithology identification by 0.95% to 11.16%, which provides a new idea for the recognition of complex carbonate lithology.
Research status and hotspot analysis of literature metrology in artificial intelligence field
Authors: Li Kai , Zhang Jianye , Li Degao 
Published: September 2021

Abstract
It is of great significance to describe the innovation track of emergency management digital research through metrology for better grasping the theoretical system and subject frontier, and promoting the practice of emergency management digital. In this paper, artificial intelligence (AI) papers published in the world from 2010 to 2019 in the SCI database of Web of Science are taken as the research object to explore the influence of countries in the field of AI research and the current hot areas, so as to provide data support and decision-making reference for AI policy making. It is found that in the process of the development of the digitalization of emergency management from scratch, a novel and complete research system has been formed, which takes the digitalization of emergency management technology as the core and takes the digitalization of emergency management organization, the digitalization of emergency management environment and the digitalization of emergency management objects as the foundation. Further studies have found that digital frontier including emergency management in large data, mobile Internet, Internet of things, technology innovation, and AI as the representative of volunteers to digital citizens in emergency management, digital and digital community, represented by the main body of innovation, and to no one digital dynamic emergency management and the digitization of emergency management as a representative of management innovation. Finally, the study concludes that the digital integration of emergency management consists of two parts: risk information and emergency response.
Mapping performance comparison of 2D SLAM algorithms based on different sensor combinations
Authors: Qu Pengtao , Su Chen , Wu Hang , Xu Xinxi , Gao Sheng , Zhao Xiuguo 
Published: September 2021

Abstract
Currently, some existing SLAM algorithms were widely applied in the field of mobile robots. However, most SLAM algorithms were tested either by simulation or using offline bags storing messages, which ignored other contributing factors influencing mapping including the changes of position and speed of the robot during it autonomous movement. The aim of this study is to investigate the solution of constructing more accurate map with high quality sensors based on freely available SLAM algorithms. Comparisons and analyses of three 2D SLAM algorithms (i.e. Gmapping, Hector SLAM and Cartographer) available in ROS were conducted to map different environments in our work using different sensor combinations of a LIDAR, a Stereo camera and an IMU, respectively. The research showed that the Cartographer algorithm has advantage over two other algorithms on constructing well-defined and informative environmental maps with minimal errors. Also, the LIDAR scan is more suitable than the ZED scan as the input of mapping algorithms, and the Cartographer combining the LIDAR scan, ZED odometry and IMU is the best solution to map in all combinations.
Multi-objective discrete strength pareto evolutionary algorithm II for multiple-product partial U-shaped disassembly line balancing problem
Authors: Lu FaYang , Liu Peisheng , Qi Liang , Qin Shujin , Xu Gongdan , Xu Zhiying 
Published: September 2021

Abstract
In disassembly industry, the disassembly efficiency closely depends on a reasonable disassembly mode and an efficient disassembly line. In the current manufacturing, the profit of multi-product partial U-shaped disassembly-line-balancing (MPUD) is relatively low and the energy consumption is high. According to the characteristics of the MPUD problem, a mathematical model for maximizing profit and minimizing energy consumption of human-robot disassembly is established. To solve the MPUD problem, a multi-objective strength Pareto evolutionary algorithm II is developed and crossover and mutation operators are improved to promote the convergence and feasibility of the algorithm. We perform the experiments on a ballpoint pen and a washing machine to simulate the disassembly process and compare the proposed algorithm with the nondominated sorting genetic algorithm II and the multi-objective evolutionary algorithm based on decomposition. Experimental results show that the proposed algorithm has obvious advantages in the most important metrics.
YOLOv5s-FCG : An Improved YOLOv5 Method for Inspecting Riders’ Helmet Wearing
Authors: Wang Pengfei , Huang Hanming , Wang Mengqi , Li Bingjun 
Published: September 2021

Abstract
Manual inspection of riders’ helmets is time-consuming and labor-intensive, low in efficiency and small in coverage. Aiming at this shortcoming, this paper proposed an improved YOLOv5s-FCG(FourLayers, CBAM attention, GhostBottleneck) helmet wearing detection method based on YOLOv5 (You Only Look Once). Based on the smallest volume of YOLOv5s in YOLOv5 series, the network was improved, the shallow feature detection layer was added, the three-scale feature detection was changed to four-scale feature detection, and the up sampling was increased by four times. Add the CBAM attention module; Use lightweight GhostBottleneck instead of Bottleneck structures. The results in our experiments show that YOLOv5s-FCG raises the average detection accuracy (mAP) by 2.0% compared with YOLOv5s on the ourselves’ riding safety helmet data set and 1.5% on the NWPU-VHR-10 public data set. The proposed algorithm not only ensured the detection rate, volume, computation and number of parameters, but also improved the detection accuracy. And it had good adaptability and generalization ability in complex road environments such as poor light and small targets.
Application of data encryption technology in simulation software
Authors: He Shaoqing , Wang Qian , Li Xiaoting 
Published: September 2021

Abstract
Due to the late start of China's industrial simulation software, the current virtual simulation market is basically occupied by foreign products, and the emerging forces are accelerating to enter.In order to help China occupy a field of simulation market, promote the research and development of simulation software, and ensure the security and stability of simulation network operation,This paper analyzes the definition, classification and principle of data encryption technology, and discusses the application of some encryption technology in simulation software [1].
Word Sense Disambiguation Method Based on Graph Model and Word Vector
Authors: Sun Tongtong , Du Yongxing , Zhou Liyong , Niu Lijing , Li Baoshan , Gong Yanzhang , Dong Jianmin 
Published: September 2021

Abstract
As a basic research of natural language processing, word sense disambiguation (WSD) has a very important influence on machine translation, classification tasks, retrieval tasks, etc. In order to solve the problem that existing disambiguation methods rely too much on knowledge base, a disambiguation method combining graph model and word vector is proposed in this paper. Firstly, in this method, the text data are preprocessed by removing punctuation marks and segmenting words. Secondly, the dependency relation is extracted by using the tool of PYLTP for dependency parsing, the words of dependency parent node are matched and the undirected graph is built, and the context knowledge of ambiguous words is selected according to the minimum path length set by the graph model. Finally, Word2Vec model is used to train Wikipedia corpus to obtain word vectors containing ambiguous words and contextual knowledge, and calculate the cross similarity of the word vector, the high mean similarity is regarded as the correct meaning of the ambiguous word. The effectiveness of the proposed method is verified by comparative experiments on the SEVAL-2007 Task# 5 dataset.
Research on Model-based Abnormal Traffic Detection Method
Authors: Han Jiaxin , Wang Xiaowei 
Published: September 2021

Abstract
Information security has become a concern of all walks of life, and anomaly detection can protect information security, so anomaly detection has become a research hotspot. In this paper, the principles of four commonly used model-based anomaly detection methods, namely, depth-based, distance-based, density-based and deep learning-based detection methods are introduced and their research status is reviewed. Analyzed the characteristics of the four methods, and finally pointed out the future development trend of anomaly detection methods and gave a conclusion.
A Quantum Multi-agent Blind Signature Scheme
Authors: Chen Jingjing , You Fucheng 
Published: September 2021

Abstract
With the development of information society, people are increasingly concerned about the security problems caused by information leakage, and digital signatures play an irreplaceable and important role in the protection of information security, which replace traditional manual signatures and seals in the network environment and have important roles such as anti-forgery, anti-tampering, anti-replay, identifiable identity and confidentiality [1]. However, with the in-depth study of quantum mechanics, it has been found that the confidentiality mechanism of classical digital signatures is under serious threat. Since quantum computing is a parallel operation with exponentially increasing operational capability, a series of complex mathematically difficult problems can be rapidly overcome. Based on the above, this paper proposes a multiple agent blind signature scheme, which adopts the key technologies such as the Unitary transformation and Hadamard gate operation, and adds multiple agent signature to quantum signature to improve the efficiency of the protocol, and adds blind signature to improve the security of the protocol. At the same time, the scheme uses quantum key distribution to ensure its unconditional security, which is applicable in systems such as electronic voting and electronic payment, and has certain anti-attack capability [2].
Yield Prediction of Household Garbage Based on SARIMA and Exponential Smoothing Model
Authors: Zhong Peiwen , Chen Zili , Hang Chuxin , Wu Shuang , Mei Liangcai , Sun Hongbo 
Published: September 2021

Abstract
Refuse classification is a waste treatment method, which creates reuse opportunities and enhances recyclable value for domestic waste. Prolonging the use cycle of renewable resources is one of the cores of China's sustainable development strategy. Analyzing the total amount of household waste can obtain the characteristics of regional garbage growth so that governments can promote the garbage classification process intelligently. This article chose the household garbage monthly yield data as the time series and extracted its seasonal information. We used RStudio software to develop the multiplicative seasonal autoregressive integrated moving average (SARIMA) model with 2 Holt-Winters exponential smoothing models (additive and multiplicative) for comparison. Seasonal factor charts explain that the seasonal effect cycle of series is 1 year, and its period of increase or decrease is 6 months. SARIMA(0,1,1)(1,1,0)12 is the optimal model for data, which is better than Holt-Winters models. The prediction accuracy of SARIMA model is 94.31%. For the next year, the SARIMA model predictions show that the growth rate of the household garbage yield is 2.65%, which is in line with the actual situation. The results of this study are valuable for the planning of waste classification policy in recent years.
A robust registration algorithm for 3D point cloud data with scale stretches and outliers
Authors: Li Jinlong , Zeng Ni , Meng Jingan , Gao Xiaorong , Zhang Yu 
Published: September 2021

Abstract
Generally, the point cloud data obtained by 3D scanner cannot express the overall information of objects at a single time due to the limited field of view, so registration algorithm is needed to obtain the complete information. For the registration of point cloud data sets with large scale stretches and outliers, this paper proposed a new iterative nearest point algorithm named Improved-ICP. In the case of isotropic point extension, the iterative reweighted least squares method is constructed by incorporating a weight function to the minimum error function. In calculation, the weight function is equivalent to increasing the weight the point pairs, and the weights are obtained by an M-estimation criterion. In the paper, the initial registration was optimized to get global convergence for the algorithm, and compared the Improved-ICP algorithm with the ICP algorithm and the Scale-ICP algorithm to verify the effectiveness of the algorithm. To demonstrate the robustness of the Improved-ICP algorithm, we performed several comparative experiments with different scales and different noise between the Scale-ICP and the proposed algorithm in the presence of abnormal points. Experiment results illustrate the Improved-ICP algorithm has high accuracy and strong robustness to scale and abnormal points.