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Knowledge representation of scientific paper data is a problem to be solved,and how to learn the representation of paper nodes in scientific paper heterogeneous network is the core to solve this problem.This paper proposes an unsupervised cluster-level scientific paper heterogeneous graph node representation learning method(UCHL),aiming at obtaining the representation of nodes (authors,institutions,papers,etc.) in the heterogeneous graph of scientific papers.Based on the heterogeneous graph representation,this paper performs link prediction on the entire heterogeneous graph and obtains the relationship between the edges of the nodes,that is,the relationship between paper and paper.Experiments results show that the proposed method achieves excellent performance on multiple evaluation metrics on real scientific paper datasets.

With the development of science and technology,people need to access a large number of scientific and technological information quickly,and scientific paper is one of the main ways to carry scientific and technological information.As an important part of scientific paper,abstract is an effective tool for readers to retrieve literature.Therefore,the quality of abstract affects the retrieval rate of paper directly.However,due to the lack of writing experience,the quality of abstracts written by many authors is not high.Automatic generation of summary for scientific paper can help the author grasp the important content of paper more effectively,so as to write high-quality abstract.At the same time,the automatically generated abstract can also control the number of words in the abstract,which can bring more content to readers and help them understand the paper better.Generating automa-tic summarization for scientific paper can help author write abstract faster,which is one of the research contents in automatic summarization.Compared with common news document,scientific paper has the characteristics of strong structure and clear logical relationship.As far as the mainstream abstractive summarization such as encoder-decoder model is concerned,it mainly consi-ders the serialized information in the document,and rarely explores the text structure information in the document.For this reason,according to the characteristics in scientific papers,this paper proposes an automatic summarization model based on the hie-rarchical structure of "word-section-document",which uses the association between word and section to enhance the level of text structure and the interaction between levels,so as to screen out the key information in scientific paper.In addition,a context gate unit is extended to update the optimized context vector,thus capturing context information more comprehensively.The experimental results show that the proposed model can effectively improve the performance of the generated summarization in the ROUGE evaluation method.

With the rapid growth of urban populations,the number of private cars has grown exponentially,which makes overwhelming traffic congestion problem become more and more acute.The traditional traffic signal control technology is difficult to adapt to the complex and changeable traffic conditions,and the data-driven methods bring new research directions for the control-based system.The combination of deep reinforcement learning and traffic control systems plays an important role in adaptive traffic signal control.First,this paper reviews the latest progress in the application of intelligent traffic signal control systems,the methods of intelligent traffic signal control are classified and discussed,and the existing works in this field are summarized.The deep reinforcement learning method can effectively solve the problems of inaccurate state information acquisition,poor algorithm robust and weak regional coordination control ability in intelligent traffic signal control.Then,on the basis of the above,this paper gives an overview of the simulation platforms and experimental setup for intelligent traffic signal control,and analyzes and verifies it through examples.Finally,The challenges and unsolved problems in this field are discussed and future research directions are summarized.

Biomarker recognition is a major basis for achieving precision medicine,which plays an important role in diagnosing complex diseases,judging disease stages and evaluating the safety and effectiveness of new drugs or therapies in the target population.As the key technology of intelligent identification of biomarkers,the prediction of the association between circular RNA and disease is the key to deeply evaluate and measure the biological process,pathological process and intervention pathological response of subjects,and is one of the effective means and approaches to practice “precision medicine”.This paper comprehensively combs and prospects the circular RNA disease association prediction model in biological big data.Specifically,it first discusses the relationship between circRNA and disease in terms of the research background,physicochemical properties and functions of circ-RNAs.Then,it investigates the public database resources of circRNAs and diseases,and summarizes four computational methods of circRNA-disease association prediction from the perspective of computational models,and analyzes their advantages and shortcomings.Finally,this paper discusses the current challenges and future possible research directions of circRNA-disease association prediction problem.

As a new network architecture,the software defined network(SDN)simplifies the network management logic by separating data plane and control plane,which is one of the popular research subjects of next-generation network.However,due to frequent link failures and other factors,it is difficult to guarantee the reliability of SDN,which is a problem well recognized in the industry.The existing SDN link failure recovery models often have the problems of long recovery delay,requiring too many flow entries and ignoring link congestion after recovery from failure.To solve these problems,this paper proposes a single-link failure recovery model(LFA-CA)based on segment routing(SR).The model employs the two heuristic algorithms of BPF and BPU to calculate a loop-free backup path during network initialization and update the congestion avoiding backup path during operation,respectively,so as to achieve fast recovery from single-link failure and congestion avoidance after failure.In this paper,massive simulation experiments are carried out to evaluate the performance of our model,and the results prove that compared with some of the existing SDN single-link failure recovery models,LFA-CA consumes less forwarding rules and has better load balancing ability after failure.

Since 2018,scholars have proposed and studied a new topic of “concept reduction” in formal concept analysis.Including unnecessary concepts,core concepts,relatively necessary concepts,and the identification of three types of concepts,and research on concept reduction algorithm.In this paper,the same effect relation is proposed and its important properties are studied.It pre-sents a simple way to identify three types of concepts through the same effect relationship,and proposes a new algorithm for concept reduction which is based on the concept lattice of the complement set of subsets of the same effect relationship.For decades,the algorithm of “reduction topic” has adopted the method of conjunctive normal form and disjunctive normal form transformation.Many scholars even said that “reduction problem” was equivalent to the transformation of conjunctive paradigm and disjunctive paradigm.This paper studies a new method to solve the “reduction problem” without using the transformation between conjunctive normal form and disjunctive normal form.This new method is of significance both in theory and in practice.It is a new attempt.There are often many “concept reduction” in a background,so it is not very meaningful to find out all the results.Gene-rally,it is necessary to find out “concept reduction” containing some concepts,and the method in this paper has particular advantages in this respect.

In the past decade,widespread data collection has become the norm.With the rapid development of large-scale data analysis and machine learning,data privacy is facing fundamental challenges.Exploring the trade-offs between privacy protection and data collection and analysis is a key scientific question.Differential privacy has become a de facto data privacy standard and has been widely studied and applied.Differential privacy technology can provide strict privacy protection for user data through a certain randomization mechanism.This paper provides a comprehensive overview of differential privacy technology and a summary and analysis of the latest progress of differential privacy.Specifically,this paper first gives a theoretical summary of differential privacy,including the central model,the local model and the shuffle model proposed in recent years.The three models are compared,and the advantages and disadvantages of different models are analyzed.Then,on the basis of the three models,some typical differential privacy mechanisms in literatures are analyzed from the perspective of algorithms.Then the current application of differential privacy technology in various fields is introduced.Finally,some new research topics about differential privacy technology are introduced,which expand the rich research direction for differential privacy technology.

With the rapid development and integration of biomedicine and information technology,massive amounts of imaging data,patient report data,electronic health records,and omics data have been accumulated rapidly in healthcare.These data are cha-racterized by complexity,heterogeneity and high dimensionality.Deep learning has the ability of complex function simulation and automatic feature learning,which can provide efficient technical support for research in medical diagnosis and drug development.Currently,deep learning has been extremely successful in medical imaging and further more,some medical imaging diagnostic systems based on deep learning have achieved performance that is even comparable to that of relevant experts.Due to the progress of natural language processing technology,deep learning has also made remarkable progress in the use of non-image data tasks.This paper first briefly describes the development of deep learning in healthcare.Subsequently,the application of deep learning model in healthcare is statistically analyzed,and some available datasets are sorted out.In addition,this paper also introduces the research progress of deep learning in medical diagnosis and treatment processes such as disease diagnosis and health monitoring,and its research progress in protein structure prediction and drug discovery.Finally,key challenges of deep learning in healthcare applications such as data quality,interpretability,privacy security and practical application limitations are discussed.It also discusses feasible solutions or approaches to these challenges.

Batched matrix computing problems are widely existed in scientific computing and engineering applications.With rapid performance improvements,GPU has become an important tool to solve such problems.The eigenvalue decomposition belongs to the two-sided decomposition and must be solved by the iterative algorithm.Iterative numbers for different matrices can be varied.Therefore,designing eigenvalue decomposition algorithms for batched matrices on the GPU is more challenging than designing batched algorithms for the one-sided decomposition,such as LU decomposition.This paper proposes batched algorithms based on the Jacobi algorithms for eigenvalue decomposition of Hermitian matrices.For matrices that cannot reside in shared memory wholly,the block technique is used to improve the arithmetic intensity,thus improving the use of GPU resources.Algorithms presented in this paper run completely on the GPU,avoiding the communication between the CPU and GPU.Kernel fusion is adopted to decrease the overhead of launching kernel and global memory access.Experimental results on V100 GPU show that our algorithms are better than existing works.Performance evaluation results of the Roofline model indicate that our implementations are close to the upper bound,approaching 4.11TFLOPS.

Knowledge Graph-to-Text is a new task in the field of knowledge graph,which aims to transform knowledge graph into readable text describing these knowledge.With the deepening of research in recent years,the generation technology of Graph-to-Text has been applied to the fields of product review generation,recommendation explanation generation,paper abstract generation and so on.The translation model in the existing methods adopts the method of first-plan-then-realization,which fails to dynamically adjust the planning according to the generated text and does not track the static content planning,resulting in incohe-rent semantics before and after the text.In order to improve the semantic coherence of generated text,a Graph-to-Text model based on dynamic memory and two-layer reconstruction enhancement is proposed in this paper.Through three stages of static content planning,dynamic content planning and two-layer reconstruction mechanism,this model makes up for the structural difference between knowledge graph and text,focusing on the content of each triple while generating text.Compared with exis-ting generation models,this model not only compensates for the structural differences between knowledge graphs and texts,but also improves the ability to locate key entities,resulting in stronger factual consistency and semantics in the generated texts.In this paper,experiments are conducted on the WebNLG dataset.The results show that,compared with the current exis-ting models in the task of Graph-to-Text,the proposed model generates more accurate content planning.The logic between the sentences of the generated text is more reasonable and the correlation is stronger.The proposed model outperforms existing methods on me-trics such as BLEU,METEOR,ROUGE,CHRF++,etc.