A Degradation Type Adaptive and Deep CNN-Based Image Classification Model for Degraded Images

Deep Convolutional Neural Networks(CNNs)have achieved high accuracy in image classification tasks,however,most existing models are trained on high-quality images that are not subject to image degradation.In practice,images are often affected by various types of degradation which can significantly impact the performance of CNNs.In this work,we investigate the influence of image degradation on three typical image classification CNNs and propose a Degradation Type Adaptive Image Classification Model(DTA-ICM)to improve the existing CNNs’classification accuracy on degraded images.The proposed DTA-ICM comprises two key components:a Degradation Type Predictor(DTP)and a Degradation Type Specified Image Classifier(DTS-IC)set,which is trained on existing CNNs for specified types of degradation.The DTP predicts the degradation type of a test image,and the corresponding DTS-IC is then selected to classify the image.We evaluate the performance of both the proposed DTP and the DTA-ICMon the Caltech 101 database.The experimental results demonstrate that the proposed DTP achieves an average accuracy of 99.70%.Moreover,the proposed DTA-ICM,based on AlexNet,VGG19,and ResNet152,exhibits an average accuracy improvement of 20.63%,18.22%,and 12.9%,respectively,compared with the original CNNs in classifying degraded images.It suggests that the proposed DTA-ICM can effectively improve the classification performance of existing CNNs on degraded images,which has important practical implications.

Image Recognition of Citrus Diseases Based on Deep Learning

In recent years,with the development of machine learning and deep learning,it is possible to identify and even control crop diseases by using electronic devices instead of manual observation.In this paper,an image recognition method of citrus diseases based on deep learning is proposed.We built a citrus image dataset including six common citrus diseases.The deep learning network is used to train and learn these images,which can effectively identify and classify crop diseases.In the experiment,we use MobileNetV2 model as the primary network and compare it with other network models in the aspect of speed,model size,accuracy.Results show that our method reduces the prediction time consumption and model size while keeping a good classification accuracy.Finally,we discuss the significance of using MobileNetV2 to identify and classify agricultural diseases in mobile terminal,and put forward relevant suggestions.

The Characteristic Polynomial of the Mixed Arrangement

We consider the mixed arrangement which is composed of the central hyperplane arrangement and a sphere. We discuss the lattice of its intersection set and the relationship between the Mobius function of the mixed arrangement and the original hyperplane arangement. The Mobius function of the mixed arrangement is equal to the positive or the negative Mobius function of original hyperplane arrangement. Moreover, we give an equality of the chambers and the characteristic polynomial for the mixed arrangement.

Effect of observation time on source identification of diffusion in complex networks

This paper examines the effect of the observation time on source identification of a discrete-time susceptible-infectedrecovered diffusion process in a network with snapshot of partial nodes.We formulate the source identification problem as a maximum likelihood(ML)estimator and develop a statistical inference method based on Monte Carlo simulation(MCS)to estimate the source location and the initial time of diffusion.Experimental results in synthetic networks and real-world networks demonstrate evident impact of the observation time as well as the fraction of the observers on the concerned problem.

MEIM:A Multi-Source Software Knowledge Entity Extraction Integration Model

Entity recognition and extraction are the foundations of knowledge graph construction.Entity data in the field of software engineering come from different platforms and communities,and have different formats.This paper divides multi-source software knowledge entities into unstructured data,semi-structured data and code data.For these different types of data,Bi-directional Long Short-Term Memory(Bi-LSTM)with Conditional Random Field(CRF),template matching,and abstract syntax tree are used and integrated into a multi-source software knowledge entity extraction integration model(MEIM)to extract software entities.The model can be updated continuously based on user’s feedbacks to improve the accuracy.To deal with the shortage of entity annotation datasets,keyword extraction methods based on Term Frequency–Inverse Document Frequency(TF-IDF),TextRank,and K-Means are applied to annotate tasks.The proposed MEIM model is applied to the Spring Boot framework,which demonstrates good adaptability.The extracted entities are used to construct a knowledge graph,which is applied to association retrieval and association visualization.

Failure Mechanism of Reflow Conductor Roll of Electroplating Tinning Line

The surface roughness of reflow conductor roll was checked on membrane sample. The surface morphology of conductor roll was observed by microscope, and the composition of adhered layer on conductor roll surface was analyzed by X-ray spectroscope. The results show that tin adhesion is the main reason for failure of conductor roll, and the failure of conductor roll is accelerated by wear. The measures to decrease tin adhesion and improve wear resistance were put forward.

Research on Urban Tourism Image Positioning:A Case Study of Dengfeng in Henan Province

Aiming at facilitating the research of urban tourism image positioning,this paper summarizes the domestic and abroad theories on urban tourism image and analyzes its significance for cities.With Dengfeng as an example,its goal is to boost development of Dengfeng tourism,which is an excellent tourism city in China.This paper presents its current urban tourism developing situation,positions its tourism image,concludes the plan of image brand and proposes promotional slogans based on the analysis of Dengfeng tourism local features,its core elements and perceptions of visitors.

The Measurement of the Software Ecosystem’s Productivity with GitHub

Software productivity has always been one of the most critical metrics for measuring software development.However,with the open-source community(e.g.,GitHub),new software development models are emerging.The traditional productivity metrics do not provide a comprehensive measure of the new software development models.Therefore,it is necessary to build a productivity measurement model of open source software ecosystem suitable for the open-source community’s production activities.Based on the natural ecosystem,this paper proposes concepts related to the productivity of open source software ecosystems,analyses influencing factors of open source software ecosystem productivity,and constructs a measurement model using these factors.Model validation experiments show that the model is compatible with a large portion of open source software ecosystems in GitHub.This study can provide references for participants of the open-source software ecosystem to choose proper types of ecosystems.The study also provides a basis for ecosystem health assessment for researchers interested in ecosystem quality.

Graph Convolutional Network-Based Repository Recommendation System

GitHub repository recommendation is a research hotspot in the field of open-source software. The current problemswith the repository recommendation systemare the insufficient utilization of open-source community informationand the fact that the scoring metrics used to calculate the matching degree between developers and repositoriesare developed manually and rely too much on human experience, leading to poor recommendation results. Toaddress these problems, we design a questionnaire to investigate which repository information developers focus onand propose a graph convolutional network-based repository recommendation system (GCNRec). First, to solveinsufficient information utilization in open-source communities, we construct a Developer-Repository networkusing four types of behavioral data that best reflect developers’ programming preferences and extract features ofdevelopers and repositories from the repository content that developers focus on. Then, we design a repositoryrecommendation model based on a multi-layer graph convolutional network to avoid the manual formulation ofscoringmetrics. Thismodel takes the Developer-Repository network, developer features and repository features asinputs, and recommends the top-k repositories that developers are most likely to be interested in by learning theirpreferences. We have verified the proposed GCNRec on the dataset, and by comparing it with other open-sourcerepository recommendation methods, GCNRec achieves higher precision and hit rate.

An Ui Design Optimization Strategy for General App in Big Data Environment

Due to the huge amount of increasing data, the requirements of people forelectronic products such as mobile phones, tablets, and notebooks are constantlyimproving. The development and design of various software applications attach greatimportance to users’ experiences. The rationalized UI design should allow a user not onlyenjoy the visual design experience of the new product but also operating it morepleasingly. This process is to enhance the attractiveness and performance of the newproduct and thus to promote the active usage and consuming conduct of users. In thispaper, an UI design optimization strategy for general APP in the big data environment isproposed to get better user experience while effectively obtaining information. Anexperimental example of a library APP is designed to optimize the user experience. Theexperimental results show that the user-centered UI design is the core of optimization,and user portrait based on big data platforms is the key to UI design.

The Water Problem and Its Solution in Gansu, China

Based on such severe situation, we need to work out a way that enables us to analyze the current and future ability of a region to provide clean water to meet the needs of its population, and to develop a reasonable strategy to optimize the utilization of water resources in this area. This paper has worked out a resolution model and input the data of China, the United States, Russia, Laos and Afghanistan to do national testing. Then, we use the policy from “diaper incident” to do policy testing. The calculation results of the model are in conformity with the reality. Therefore, the model is effective. At last this model is used to resolve Gansu’s water problem and provide effective advices for the local government.

Measuring time accessibility and its spatial characteristics in the urban areas of Beijing

Construction of road infrastructure is fundamental to city operation and development, as well as an important pathway and focus in physical urban-rural integration. The long-term implementation of a system of ring roads plus radiating roads in Beijing has strongly impacted urban infrastructure construction and space-time accessibility. Particularly, recent rapid growth of private car ownership in Beijing has imposed greater loads on its road system, seriously hampering urban commuting efficiency and negatively impacting quality of life. To address such challenges and enhance the rapid development of transport infrastructure, Beijing has accelerated rail transit construction since 2008 in an effort to improve commuting capacity. This paper aims to measure time accessibility and its spatial characteristics in urban areas of Beijing by applying a comprehensive method that combines vector and raster attribute data generated from road network and subway transport infrastructure. By using a dual index of accessibility and road density, the study further reveals the features of and differences in spatial accessibility and the construction of road systems in urban areas of the northern and southern parts of Beijing. The findings of this study can provide a scientific basis for future urban planning and road system construction both in general and with respect to Beijing, given its aspirations to become a world city.

A Model of Grid Service Capacity

Computational grids （CGs） are large scale networks of geographically distributed aggregates of resource clusters that may be contributed by distinct organizations for the provision of computing services such as model simulation, compute cycle and data mining. Traditionally, the decision-making strategies underlying the grid management mechanisms rely on the physical view of the grid resource model. This entails the need for complex multi-dimensional search strategies and a considerable level of resource state information exchange between the grid management domains. In this paper we argue that with the adoption of service oriented grid architectures, a logical service-oriented view of the resource model provides a more appropriate level of abstraction to express the grid capacity to handle incoming service requests. In this respect, we propose a quantification model of the aggregated service capacity of the hosting environment that is updated based on the monitored state of the various environmental resources required by the hosted services. A comparative experimental validation of the model shows its performance towards enabling an adequate exploitation of provisioned services.

EXPLORATION/EXPLOITATION TRADEOFF WITH CELL-SHIFT AND HEURISTIC CROSSOVER FOR EVOLUTIONARY ALGORITHMS

In order to tradeoff exploration/exploitation and inspired by cell genetic algorithm a cellshift crossover operator for evolutionary algorithm （EA） is proposed in this paper. The definition domain is divided into n-dimension cubic sub-domains （cell） and each individual locates at an ndimensional cube. Cell-shift crossover first exchanges the cell numbers of the crossover pair if they are in the different cells （exploration） and subsequently shift the first individual from its initial place to the other individual＇s cell place. If they are already in the same cell heuristic crossover （exploitation） is used. Cell-shift/heuristic crossover adaptively executes exploration/exploitation search with the vary of genetic diversity. The cell-shift EA has excellent performance in terms of efficiency and efficacy on ten usually used optimization benchmarks when comparing with the recent well-known FEP evolutionary algorithm.

Causal Analysis of Futures Sugar Prices in Zhengzhou

In this paper, we are interested in investigating the causal relationships among futures sugar prices in the Zhengzhou futures exchange market （ZF）, the spot sugar prices in Zhengzhou （ZS） and the futures sugar prices in New York futures exchange market （NF）. A useful tool called Bayesian network is introduced to analyze the problem. Since there are only three variables in our Bayesian network, the algorithm is straightforward： we display all the 25 possible network structures and adopt certain scoring metrics to evaluate them. We applied five different scoring metrics in total. Firstly, for each metric, we obtained 24 scores, each calculated from one of the 24 possible structures i.e. a Directed Acyclic Graph （DAG）. Then we eliminated the network structure which represents the independence of the three variables according to our prior knowledge concerning the futures sugar market. After that, the optimal network structure which implies the causal relationships was selected according to the corresponding scoring metric. Finally, after comparing the results from different scoring metrics, we obtained the relatively affirmative conclusion that ZS causes ZF from both the Bayesian Dirichlet （BD） metric, Bayesian Dirichlet-Akaike Information Criterion （BD-AIC） metric, Bayesian Dirichlet-Bayesian Information Criterion （BD-BIC） metric and Bayesian Information Criterion （BIC） metric. The conclusions that NF causes ZF and ZF causes ZS from the Akaike Information Criterion （AIC） metric and ZF causes ZS from the BIC metric were useful and significant to our investigation.

Prognostic for fuel cell based on particle filter and recurrent neural network fusion structure

Fuel cells are considered as one of the most promising candidates for future power source due to its high energy density and environmentally friendly properties,whereas the short lifespan blocks its large-scale commercializa-tion.In order to enhance the reliability and durability of proton exchange membrane fuel cell,a fusion prog-nostic approach based on particle filter(model-based)and long-short term memory recurrent neural network(data-driven)is proposed in this paper.Both the remaining useful life estimation and the short-term degradation prediction can be achieved based on the prognostic method.For remaining useful life estimation,the particle filter method is used to identify the model parameters in the training phase and the long-short term memory recurrent neural network is used to update the parameters in the prediction phase.As for short-term degradation prediction,the particle filter and long-short term memory recurrent neural network are firstly trained individually in the training phase and then be fused to make predictions in the prediction phase.The proposed fusion structure is validated by the fuel cell experimental tests data,and results indicate that better prognostic performance can be obtained compared with the individual model-based or data-driven method.