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The safety and stability of power networks have become increasingly important in the field of industrial control. Traditional information analysis for power networks overly relies on expert knowledge, and existing analysis models suffer from problems such as high algorithm complexity and rule redundancy. To address these issues, this paper proposes an advanced alarm information correlation analysis method that takes into account the characteristics of power networks. The method first eliminates noisy parts in the original alarm logs through a pre-processing module, then generates alarm transaction sets using a proposed method based on dynamic sliding time window, and subsequently applies the FP-Growth algorithm to mine alarm association rules for power networks. Finally, a time-based alarm rule filtering algorithm is proposed to eliminate invalid rules. Experiments conducted on alarm data collected from a situation awareness platform deployed in a power grid company show that this method reduces the redundancy of alarm rules by an average of about 30% compared to other similar association analysis method, and can effectively extract key alarm rules in power networks to guide fault warning. [ABSTRACT FROM AUTHOR]

Green's theorem is widely used in physics, hydrodynamics, chemistry and other fields. Traditional computer simulation and numerical calculation methods are usually used to build the system model based on Green's theorem. However, the possible system defects in the tool software lead to the deviation of the model, which makes the task fail. In order to solve the above problems, this paper adopts the formalization method based on higher-order logic to realize the higher-order logic modeling and verification of Green's theorem related content in the theorem prover HOL Light. Firstly, the basic concepts and properties of gradient and divergence are formally described. Secondly, formal modeling and verification of Green's theorem and its properties are carried out. Finally, the high-level logical derivation of groundwater control model is completed based on the formal model of Green's theorem, so as to ensure the safety of the system model. [ABSTRACT FROM AUTHOR]

This paper proposes an arithmetic optimization algorithm that integrates the sine-cosine strategy to address the problems of low solution accuracy, slow convergence speed, and easy fall into local optima in arithmetic optimization algorithms. The algorithm adaptively adjusts the math optimizer acceleration (MOA) accelerator function based on the change information of individual fitness, balancing the global exploration and local exploitation abilities of the algorithm. The improved sine-cosine algorithm is introduced into the local development stage of the algorithm, increasing the population diversity in the later iterations, avoiding the algorithm from falling into local optima, and effectively improving the solution accuracy and convergence speed of the algorithm. Simulation experiments on 14 benchmark test functions show that the improved algorithm has significant improvements in solution accuracy, convergence speed, and robustness. Finally, the improved algorithm is applied to the optimization of support vector machine (SVM) parameters, and a student knowledge level prediction model is established, which further verifies the practicality and superiority of the algorithm. [ABSTRACT FROM AUTHOR]

Existing object detection algorithms have problems with false positives and false negatives when detecting small hats in complex scenes. In this paper, a multi-stage adaptive hat detection algorithm (MAHD) is proposed. Firstly, a region proposal network (MA RPN) based on adaptive convolution is constructed, and the features of anchors are refined through multiple stages to improve the algorithm's ability to recognize targets in complex backgrounds. Then, an adaptive sampling strategy is used to dynamically allocate positive and negative samples, and the focus loss function is combined to guide the training of MA RPN and improve the detection accuracy of small targets. Finally, experiments re conducted on a self-built HAT 4.5k dataset. The results show that compared with the Grid R-CNN algorithm, the proposed algorithm improves AP by 2.6% and APS by 5.1%. The detection performance of small targets is further verified on the open-source VisDrone-DET 2019 dataset, demonstrating he feasibility and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

To meet the fast iteration speed and high computing power requirements of deep learning models, mainstream hardware vendors are increasingly inclined towards heterogeneous systems consisting of general-purpose processors and AI-specific accelerator cores. However, AI-specific accelerator ores only support certain core operators and do not have general programming capabilities. Therefore, how to efficiently deploy deep learning tasks on such heterogeneous architectures is worth further research. Based on the domestically developed FT-Matrix DSP+MatrixZone heterogeneous system platform, this paper designs and implements a deep learning programming framework, called KaiSa. KaiSa analyzes the input parameters of the deep learning model, identifies the operator type, and assigns it to the corresponding computing core. For complex operators, KaiSa automatically completes the optimal search for the block size based on a performance model, improving the performance of dual-core parallel computing. At the same time, KaiSa shields all low-level hardware details to provide users with a friendly programming environment for efficient program development. Experimental results show that KaiSa can achieve performance improvements of up to 39.0%. [ABSTRACT FROM AUTHOR]

Aiming at the erroneous literacy caused by irregular interference and text adhesion in text images, this paper proposes a text image restoration model based on partial convolution operations. This paper studies and analyzes the text image characteristics of several common fonts, establishes a text image database, and integrates it with the interference mask database, then evaluates the repair effect of the model, and conducts classification tests on different levels of repair. Experiments have proved that the text model predicts the missing parts based on the existing parts of the current text under the premise of ensuring that the original text information is not lost. The peak signal-to-noise ratio is up to 32. 46 dB, the structural similarity is up to 0. 954, and the best loss value is up to 0. 015. The text recognition rate after repair is 27. 85% higher than that before repair. After repairing the defective pictures of four ancient scripts, including official script, seal script, oracle bone inscriptions, and running script, the peak signal-to-noise ratio reached 30. 46 dB, and the structural similarity reached 0. 964. [ABSTRACT FROM AUTHOR]

This paper investigates the research status of automatic circuit lay out and routing technology at home and abroad, and designs a layout and routing algorithm for medium-sized circuits. It is mainly used in the module test of large-scale layout design software, and provides users with preliminary wiring and lay out results of each module, which is convenient for users to find and correct error points efficiently, and fills the gap in related fields in China. In this paper, a hypergraph model is established and transformed into a graph model. The Stoer-Wagner algorithm is improved, and the algorithm and the Fiduccia-Mattheyses algorithm are used to divide the graph based on the minimum cut theory, and construct a partition tree. Based on this tree, this paper designs a binary relative moving algorithm to determine the location of each circuit component, which greatly reduces the layout congestion and improves the aesthetics. For a circuit with hundreds of components, its layout can be obtained within 0. 5 seconds. Based on the A* algorithm, the routing is improved in many aspects, and the routing speed is improved. For a circuit with <1000 wires, the routing resuIts can be obtained within 0. 1 s to 60 s, achieving 100 % routing rate and uniform layout and routing effect. [ABSTRACT FROM AUTHOR]

Domestic heterogeneous many-core processors are a key link for my country to break international technical barriers and make breakthroughs in the field of high-performance computing. Focusing on the construetion of domestically produced super-computing software ecological environment, using the intelligent source code conversion method to revitalize the massive multi-core architecture legacy code is an important way to accelerate the efficiency of software development and promote the development of the field. Aiming at the current situation that domestic computing cores do not support C++ compilation, based on the open source ANTLR language translation tool, this paper proposes an auxiliary framework of the intelligent C++ to C conversion for heterogeneous many-core processors. The framework focuses on the key features of object-oriented languages. Based on the abstract syntax treem it implements base classes and inheritance classes, function definitions, template instantiation based on tagging, and C language conversion of some STL libraries, and establishes automatic annotation of the code to be converted. The system greatly improves the conversion and transplantation efficiency of C ++ code. The automatic conversion and transplantation test of the measurable parallel computing benchmark application BableStream confirms the effectiveness of the conversion method proposed in this paper. [ABSTRACT FROM AUTHOR]

Weighting idea and singular blending technology are used to extend the traditional quasi Bezier curve, and a singular blending quasi-Bezier curve with shape parameters is constructed. Firstly, the singular Mending function and the quasicubic Bezier basis function of the triangular polynomial space are combined to obtain the definition of the singular blending quasi-Bezier curve, and the singular Mending quasi-Bezier bass function is deduced according to the definition of the singular blending quasi-Bezier curve. Secondly, we discuss the singular Mending quasi-Bezier bass functions and the properties of their corresponding curves, and explore the influences of singular Mending and parameters on them. Finally, an example of a singular Mending quasi-Bezier curve and surface design is given. The experimental results show that the curve constructed in this paper has the flexible shape adjustability while having the practical properties of the traditional Bezier curve. The new curve can not only accurately represent conic curves such as elliptical arc, circular and parabola arc, but also achieve G1 and G2 continuity under certain conditions. Extending the curve to the surface using the tensor product method can also accurately represent the ellipsoid and the spherical surface. A large number of analyses and examples prove that the curves constructed in this paper are very effective in geometric design. [ABSTRACT FROM AUTHOR]

The cloud-of-clouds storage system provides a cloud storage resource management plat¬form that is widely deployed in different online application scenarios. By encrypting and distributing user data to multiple clouds' the cloud-of-clouds storage system can ensure the safety and rehabilitee of stored data. In order to efficiently manage various resources of the cloud-of-clouds storage system' the cloud-of-clouds storage system has designed different data distribution schemes to meet application requirements. From the perspective of storage performance optimization' this paper summarizes the main application scenarios system functions and corresponding implementation schemes of current cloud-of-clouds storage. Firstly' this paper introduces the background of the cloud-of-clouds storage system and the current main data distribution schemes. Secondly' this paper compares the current mainstream inter cloud storage system network transmission and resource management solutions. Among them it includes the analysis of the main network transmission schemes in the data read and write and hear operations of the cloud-of-clouds storage system and the current cloud-of-clouds storage system's strategy for and cloud resources. Finally' this paper summarizes the current main application scenarios of cloud-of-clouds storage and corresponding system implementation schemes. On this basis' this paper analyzes the problems to be solved in the current cloud-of-clouds storage system and the challenges it brings, and gives possible system solutions. [ABSTRACT FROM AUTHOR]

Direct connection of multi-core processors to build multi-way parallel systems has always been the main way to improve the parallelism of high-performance computers. This paper mainly studies the QoS design and verification of the multi-core processor's direct connection interface. Through the direct connection interface, the cache consistent message across the chip can be effectively and reliably transmitted, and the SMP system (symmetric multiprocessing) sharing main memory can be realized. In this paper, the key technologies of QoS design for each protocol layer of direct connection interface are described in detail. After the validity of QoS design is verified by the reusable verification platform based on UVM method, it has been transplanted to the FPGA prototype verification platform and passed the test successfully. In order to improve the performance of multi-way servers, it is necessary to further study the direct connection technology of multi-core processors, which has good application and research prospects. [ABSTRACT FROM AUTHOR]

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Aiming at the problem that the existing deep convolutional neural network has a large amount of parameters, which leads to the limitation of facial expression recognition scenes, this paper proposes a facial expression recognition model based on improved lightweight convolutional neural network. The model takes MobileNetV2 lightweight feature extraction network as the main framework, by compressing the network width factor and the global dimension, the number of network parameters and the amount of computation are reduced. Sand Glass block is introduced to improve the reverse residual module in this network, and reduce the loss of feature information during network transmission. At the same time, the efficient channel attention mechanism is embedded to improve the network’s ability to extract feature information. Experiments were carried out on the facial expression data sets FER2013 and CK+. The facial expression accuracy rate of the proposed network reaches 68.96% and 95.96%, which are 1.06% and 6.14% higher than that of MobileNetV2 respectively, and the number of parameters are decreased by 82.28%. Experimental results verify the effectiveness of the improved network model. [ABSTRACT FROM AUTHOR]

Nowadays, smartphone users install dozens or even hundreds of Apps on their phones. Predicting App usage not only helps the mobile phone system to speed up App launching but also reduce the time for users to search App. This paper focuses on a novel session-based App usage prediction problem that tends to predict a sequence of Apps to be used in a period. A session-based embedding framework called SEM is proposed to solve the problem. Aiming at the side length of application session and the heterogeneity of session semantics, a session embedding method is proposed to form uniform feature representation, which alleviates the problem of user sparsity and obtains the vector representation of sessions. Based on session embedding, a two-layer GRU-based recursive neural network model is trained for App usage session prediction. Extensive experiments based on real datasets show that the proposed framework outperforms conventional App recommendation approaches. [ABSTRACT FROM AUTHOR]

With the rapid development of the technology of computer vision and autonomous driving, the ability to sense, understand and predict human behavior is becoming more and more important. The popularity of various sensors has generated a large amount of position data of moving objects in society. Predicting the movement trajectory of pedestrians based on these data has great value in social prediction and other fields. To gain insight into the development in this area, a literature review is conducted on graph neural network-based pedestrian trajectory prediction methods. The graph neural network algorithms for pedestrian trajectory prediction are compared, analyzed and summarized from multiple perspectives, and the research and development of different algorithms in this field are discussed. The comparison and analysis are carried out on the current public data sets, an overview of the corresponding performance indicators is provided, and the performance comparison results of different algorithms are given. At the same time, this paper puts forward the research problems that still exist and looks forward to the possible research directions in the future. [ABSTRACT FROM AUTHOR]

Goal-conflict analysis of divergences in requirement engineering aims to identify, assess, and resolve divergences. The divergence is caused, because the mismatch between domain attributes and objectives makes the system unable to satisfy all objectives at the same time under boundary conditions. Boundary conditions describe disagreements in the form of linear temporal logic. Due to the lack of interpretability of arbitrary boundary conditions and the extensive manual evaluation and design required to evaluate and repair divergences, the current definition of boundary conditions is not conducive to efficient and automated evaluation and repair of divergences. Therefore, in this paper, an explainable boundary condition, called lasso boundary condition, is proposed. Lasso boundary condition intuitively describes the situation where the system diverges due to some specific preconditions. Then, a lasso boundary condition identification algorithm (LBC identifier, LBCI) based on gradual weakening is designed. LBCI gradually satisfies the boundary conditions by weakening the linear temporal logic formula. The effectiveness of lasso boundary condition and LBCI are evaluated on a baseline data set. The experimental results show that the lasso boundary condition can enhance the interpretability of the divergences and the guiding role in repairing the divergences. [ABSTRACT FROM AUTHOR]

Gas chromatography data of oil-immersed transformers is a kind of multivariate time series, but such data is often missing due to equipment or network failures. Imputation is usually required to form a complete dataset for further business analysis and research. However, the existing imputation models cannot deal with multivariate time series data conveniently to guarantee the efficiency and effect from the inherent characteristics of temporal irregularity and temporal bidirectionality. In this paper, a model Conv-WGAIN is proposed based on the Generative Adversarial Imputation Nets (GAIN). Through the constructed imputation feature map, 2D convolution can be used to learn temporal bidirectional features and simultaneously deal with irregular time intervals. The Wasserstein distance is introduced in discriminator for judgement to improve the stability of the model. Experiments on gas chromatography datasets from a real project and 3 public datasets show that our work is universal for data imputation on multivariate time series missing, and Conv-WGAIN outperforms other baselines with 20.75% to 73.37% in metric RMSE. [ABSTRACT FROM AUTHOR]

The curvilinear motion of solar photosphere bright points, which is approximate rotation, is of great significance for studying how the energy from solar convection zone is transmitted to the corona. The existing algorithms only detect the global curvilinear motion of photosphere bright points. This paper proposes a multi-scale and multi-modal deep learning method to detect the global and local curvilinear motion of photosphere bright points. This mothod constructs a multi-scale network model based on the bidirectional long short-term memory network (Bi-LSTM) to extract multi-scale time sequence features of the trajectories of photosphere bright points. EfficientNet-B0 is adopted to extract the spatial features of the trajectories. The temporal features and spatial features are fused into multi-modal features to detect various curvilinear motions of photosphere bright points. The experiment results show that the accuracy of this method is 85.08%, which is 6.12% higher than that of the single-scale method and 3.1% higher than that of the multi-scale and single-mode method. This method can also be applied to the motion type detection requirements in other fields. [ABSTRACT FROM AUTHOR]

Attention mechanism has recently shown superior performance in deep neural networks, its computation generates complex data flow and requires high computation and memory overheads. Therefore, customized accelerators are required to optimize the inference computing. This paper proposes an accelerator architecture for attention mechanism computation. A flexible partitioning method based on hardware control is used to divide the huge matrices in the attention model into hardwarefriendly computing blocks, which realizes the systolic array in accelerator matched by the block computation match. A layer fusion computing structure based on two-step softmax function decomposition is proposed, which effectively reduces the memory access of attention mechanism computation. A fusedlayer attention model accelerator based on fine-grained computational scheduling is designed and implemented by HDL. The performance was evaluated based on the XLINIX FPGA device and HLS tool. Compared with the CPU and GPU implementation under the same settings, the delay of accelerator was improved by 4.91 times, the efficiency of accelerator was improved by 1.24 times. [ABSTRACT FROM AUTHOR]

The powerful ability of graph neural network to process non-Euclidean spatial data has prompted more and more people to pay attention to its application in the recommendation field. However, most of the existing recommendation models based on graph neural networks still use several adjacency matrices to represent heterogeneous information such as all kind of nodes or edge attributes, and fail to make full use of the interaction of heterogeneous information. Therefore, this paper proposes a new graph neural network recommendation model, which models the rich interactions between all information entities as heterogeneous graph and uses the dense subgraph sampling strategy for sampling the subgraphs of heterogeneous graph. In addition, the multi-task learning method is added to the model to jointly optimize the link prediction and recommendation tasks, so that the model learns a better node representation and effectively improves the recommendation results. Experiments on two public datasets show that, compared with the baseline models, the proposed model improves the performance of the Top-N recommendation task. [ABSTRACT FROM AUTHOR]

In view of the low accuracy and slow speed of the traditional recognition methods for the surface defects of solar panels, this paper proposes a method based on improved InceptionV3 and transfer learning. Firstly, image preprocessing is carried out on the collected solar panels. Secondly, a new loss function is introduced to improve the InceptionV3 neural network by using the balance factor δ to ensure the recognition rate of the network. Finally, a defect recognition model is established with the transfer learning method to further improve the performance. The simulation results show that the method can effectively improve the defect recognition accuracy and speed of solar panels. The recognition accuracy is up to 96.43%, which is 2.45% higher than the traditional InceptionV3 model, and the average classification time is shortened by 4.5 ms. The experimental results show that this method has good effect and has great application prospect. [ABSTRACT FROM AUTHOR]

Component-based software system is a system whose core is structure design. Determining the possible weak components of the system and eliminating the potential dangers in the design stage are of great significance to ensure the quality of the software system and reduce the waste and loss of resources caused by blind development. In this paper, two system parameters, total number of system faults and detection rate of system faults are defined for the software system whose component reliability follows the G-O model, and two system parameter estimation models based on the corresponding component parameters are established. On this basis, a method to determine the weak components of the system is given, and the effectiveness of the proposed method is verified by simulation. This method can prejudge the components that are most likely to be detected faults in the test or operation environment. Therefore, it has reference value for assisting software designers to determine weak components and optimize structure design. [ABSTRACT FROM AUTHOR]

Sparse multivariate polynomial interpolation is an effective strategy to reconstruct black box functions by using the sparse structure of polynomials and the given interpolation point information, which is widely used in science and engineering fields. The complexity of the traditional sparse interpolation algorithm based on Prony method is related to the number and degree of polynomial terms, and the efficiency is low when en-countering large-scale problems due to the execution of multiple high-order algebraic operations. Therefore, this paper proposes a sparse multivariate polynomial interpolation algorithm based on polynomial coefficient parsing. The core operation is to resolve the coefficients of univariate polynomials by using modular arithmetic, which avoids the solution of higher-order equations and finding the roots of higher order equation in the traditional method. In this method, black box polynomial is regarded as a univariate polynomial with one variable as the principal component, and the multivariate polynomial is recovered by parsing the values of the coefficient polynomials of the principal component at different interpolation point. Theoretical analysis and numerical experiments show that the algorithm is effective and feasible. [ABSTRACT FROM AUTHOR]

IO Die can be used as an IO extension chip, or a chiplet, which can be reused for multiple projects. This paper proposes an agile verification method of an IO Die. In order to reuse cross-level test cases, three levels (sub-system level, cluster level and whole chip level) are realized on the verification platform, and test cases at each verification level are optimized. Coverage drive, configurable constraint generation and multi-objective optimization methods are adopted respectively to improve the generation efficiency of test cases. Experimental results show that, this method can reduce the cost and time and effectively achieve Known Good Die (KGD) on the premise of ensuring efficiency and reliability. [ABSTRACT FROM AUTHOR]

The SPEC CPU2017 benchmark package, which includes the next generation of industry standards, is one of the objective and trusted benchmarks for evaluating CPU performance today. This paper uses SPEC CPU2017 to test Intel Xeon Gold 6252N CPU with Intel Cascade Lake architecture under different memory frequencies, different copies, and turning on/ off Turbo, and summarizes the performance of different applications in different configuration combinations. Meanwhile, this paper also compares and tests the Intel Xeon ES-2692 V2 CPU of Intel Ivy Bridge architecture and the Intel Xeon ES-2620 V3 CPU of Intel Haswell architecture. By introducing the concept of PER, we analyze the impact of the increase of hardware features of the three architectures on the performance of the application. [ABSTRACT FROM AUTHOR]

Reliability research is a classic problem in the field of high-performance computing. With the continuous development of process technology and integrated technology, the current scale of the entire system has grown exponentially, which has brought great challenges to reliability research, especially failure analysis. This paper collects 203510247 pieces of work failure log information after the operation of the independent high-performance computing system, from January 28, 2016 to December 6, 2016. Firstly, the K-Means clustering method is used to classify the faults and analyze the fault distribution characteristics. Secondly, based on the clustering results, a time- based fault analysis model FDLSTM is designed. After training with structured logs, the occurrence time and space of different fault types are predicted. The results show that the accuracy of the proposed FD-LSTM prediction model can reach 80. 56%. The research in this paper shows that, compared with the traditional fault analysis model, in terms of time prediction and spatial prediction, the time series model FD-LSTM based on log information have practical guiding significance in improving the accuracy of fault analysis, enhancing the efficiency of machine operation and maintenance, improving the rationalization of collaborative whole system design, and other aspects. [ABSTRACT FROM AUTHOR]

Research shows that datacenter backup batteries have shown great potential in peak power shaving. The use of batteries for peak power shaving can greatly improve the power usage efficiency of datacenter, thereby saving a lot of construction cost of datacenter power infrastructure. However, due to the accelerated battery aging phenomenon, the battery often needs to be replaced several times during the life cycle of a data center. This makes the battery cost an important part of the cost of the data center. Under the more advanced distributed backup system, the battery cost accounts for a greater proportion. Therefore, how to use batteries more economically has become a key issue for cost saving. In this paper, a revenue model for predicting battery availability is proposed, which can evaluate whether the aging battery in the traditional sense still has practical value, and how to balance the performance degradation and backup reliability problem in the aging battery use. This paper also proposes an optimized battery control method, which reduces the cost of data center backup power. [ABSTRACT FROM AUTHOR]

At present, the port rate of the mainstream high-performance interconnection network reaches 100~400 Gbps, and the single channel rate reaches 25~50 Gbps. For the data transmission at this rate, Forward Error Correction (FEC) coding is a necessary technology to improve the reliability. The Ethernet international standard IEEE 802. 3 uses Forward Error Correction codes RS(528,514) and RS(544,514), but these two code types are difficult to meet the performance requirements of high-performance interconnect networks in terms of low latency. Firstly, this paper analyzes the encoding and decoding structures of RS code and quantitatively studies the relationship between RS code type parameters and encoding and decoding delays. Secondly, a new coding type RS(271,257) for low-latency high-performance interconnection network is proposed, and its advantages and disadvantages in bandwidth consumption and error correction capability are compared. Finally, based on RS(271,257), this paper implements the network coding sub-layer and performs the resource consumption evaluation and delay performance simulation. Considering the factors of resource consumption, error correction ability and delay performance, RS (271, 257) is an ideal low-latency forward error correction coding type, which can meet the design requirements of the current HPC-oriented low-latency high-performance interconnection network coding sub-layer. [ABSTRACT FROM AUTHOR]

Memristor is an enabling device with non-volatile resistance, low power consumption, high durability, ease of integration, and CMOS compatibility. The stateful logic of memristors can realize the true fusion of computing and storage, and is complete in logic, which is expected to break the limitation of Von Neumann architecture and effectively alleviate the memory wall bottleneck. These excellent properties gain memristors great interest from academia and industry. In light of this, this paper summarizes the research progress of application-oriented computing storage fusion architecture based on stateful logic. Firstly, the implementation principle and improvement method of state logic are analyzed in detail. Secondly, the state logic design based on the memristor crossbar is reviewed, including the parallel implementation of the basic logics, copy operation and comparison operation, and then the design principle and implementation structure of the data storage structure based on the memristors are summarized. The paper then revisits an application-oriented computing storage fusion architecture in detail, and finally summarizes the problems in the research of this direction, and looks forward to the future direction. [ABSTRACT FROM AUTHOR]

HMAC-SHA1 is a widely used user password authentication mechanism, and efficient password recovery for HMAC-SHA1 is of great significance. In terms of password recovery, FPGAs have more advantages than traditional CPU and GPU platforms. Therefore, this paper uses a multi-core FPGA to perform password recovery for HMAC-SHA1. The HMAC-SHA1 password processing algorithm is analyzed, and the core operation SHA1 is implemented and optimized by pipeline, shortening the critical path, and introducing the Carry-Save Adder (CSA). HMAC-SHA1 password processing operator is implemented based on full pipeline and state machine modes. Finally, the password recovery architecture is designed and implemented. The experimental results show that the throughput of SHA1 implemented in this paper is 245. 76 Gbps. The password recovery speed on the hardware platform of single board quadruple-core FPGA is 72 times faster than that of CPU and 2. 6 times faster than that of GPU. [ABSTRACT FROM AUTHOR]

Numerical simulation of the global atmospheric circulation is one of the main tools to understand the formation and dynamic behaviors of global climate，and it is also a great challenge to port and optimize such a complex application onto large scale heterogeneous platforms. Tend_lin is the hot spot of the dynamic core of IAP AGCM-4 (the 4th generation of IAP atmospheric general circulation model)，and it has a low compute-to-communication ratio. The paper ports Tend_lin to SunWay Taihulight (a large scale heterogeneous computing platform) using two different parallel application programming interfaces. The paper introduces how to parallelize the program using a data-driven parallel application programming interface AceMesh, the task parallelization method of computation loops and MPI communication, how to relax the sharing of the communication resources, and the task mapping differences between a single-level task graph and a nested task graph. The experimental results show that AceMesh can attain more than 2 times speedups compared with the OpenACC version when using 16 to 1 024 processes. The paper analyzes and explains the reasons of the performance improvement. [ABSTRACT FROM AUTHOR]

Quantum computing is a frontier research field in high performance computing，and photon system is one of the important ways to implement quantum computing. In optical quantum computings the entangled photon source is used to generate photons to encode quantum information, and its performance affects the quality and quantity of quantum bits directly. This paper systematically studies the entangled photon source based on spontaneous parametric down conversion (SPDC)，which is the most popular technique to this aim，analyzes the performance model to study purity and brightness of entangled photons generated by type-I SPDC，and derives the key condition to obtain multi-photon entanglement via two-photon entanglement. Meanwhile, using barium metaborate, this paper realizes two photons entangled based on type-I SPDC，and mainly verifies our performance model. The model and the experiment results provide theoretical and technical support for generating more quantum bits in optical quantum computing system. [ABSTRACT FROM AUTHOR]

High Performance Computing (HPC) delivers huge processing performance, and plays a key role in the national welfare and the people s livelihood. As the source of computation power, high performance processors mostly determine the overall performance of a HPC system, and are being intensively studied in many countries nowadays to maintain a competitive edge in the HPC domain. This paper provides a review of the state-of-the-art processor architectures from mainstream processor designers like NVIDIA，Intel, and AMD. We carry out the review in terms of computation resource organization, memory subsystem design, and the connection technology among multi-cores. Based on the analysis, we summarize the mainstream trends in high performance processor design. We believe that this paper can serve as a reference for future research on HPC oriented processor designs. [ABSTRACT FROM AUTHOR]

Knowledge Graph (KG) owns rich structured information, which can effectively alleviate the sparsity and cold start problem of recommendation model and improve the accuracy and interpretability of recommendation. In recent years, the end-to-end recommendation models incorporating knowledge graph have become a technological trend. This paper proposes a KG recommendation model incorporating the influence effects of similar users. The proposed method expands the interaction between users and items to effectively utilize the knowledge graph. Firstly, a graph neural network neighbor aggregation strategy and an attention mechanism are used to capture two higher-order representations of users and items on the knowledge graph respectively. Then, an influence enhancement layer is designed to capture potential representations of similar users􀆳 influence effects according to their influence effects. Finally, these three representations are fed together into a multi-layer perceptron to output prediction scores. Experimental results on real datasets show the effectiveness and efficiency of the proposed model. [ABSTRACT FROM AUTHOR]

Aiming at the problems of edge confusion caused by multiple objects gathering in remote sensing image, unclear segmentation of small scale objects, and insufficient global information acquisition in semantic segmentation process, this paper proposes a semantic segmentation algorithm of remote sensing images based on mixed attention and full-scale skip connection network, called DU-net. In this algorithm, U-net3+ is used as the basic network, and full-scale skip connection network is used as the feature extraction network. The depth supervision in the original model is abandoned, the association between feature and attention mechanism is established, and the process of semantic segmentation is finally realized. The experimental results show that the DU-net algorithm has significant improvement over the classical algorithm under different indexes, and improves the quality of image edge segmentation and the accuracy of the algorithm for small scale target segmentation. [ABSTRACT FROM AUTHOR]

Grover algorithm is a quantum search algorithm that can find the target state efficiently. However, with the increase of the amount of searching data, Grover􀆳s quantum circuit is faced with the complex gate decomposition problem. In today􀆳s NISQ era, resources are very limited, so circuit depth is an important metric. This paper introduces a two-stage quantum search algorithm based on divideand-conquer, which can run quickly in parallel on a quantum computer. A circuit optimization method is proposed to reduce the number of iterations by using block-level oracle circuit. Combining this method with divide-and-conquer idea, it is defined as 2P-Grover algorithm. The simulation experiment was carried out on the quantum computing framework Cirq. The experimental results show that, compared with Grover algorithm, the 2P-Grover algorithm can reduce the circuit depth by at least 1.2 times and maintain a high probability of search success. [ABSTRACT FROM AUTHOR]

This paper describes the design of a high-performance embedded processor with high reliability, based on the self-developed Shenwei Instruction Set Architecture (ISA). The processor adopts System-on-Chip (SoC) design technology and AMBA on-chip bus. The third generation of self-developed Shenwei high performance 64-bit CPU core, namely Core3, and multiple standard I/O interface modules including PCIe2.0 and USB2.0 are integrated on the chip. The processor is manufactured using domestic mature process technology, and integrates more than 250 million transistors. The processor can run stably with a core frequency of 800 MHz under a wide environment temperature range (-55℃~ 125°C). The peak performance of double-precision floating-point number is up to 3.2 GFlops and the maximum power consumption is less than 3.2 W. In order to achieve the design goals of high reliability, low power consumption and high performance, the technical methods and means of the chip structure design, reliability design, low power design and physical implementation are introduced in detail, and the test results of the main technical indexes such as chip frequency, power consumption and yield are given. The processor has been applied in many information devices and has achieved good social benefits. [ABSTRACT FROM AUTHOR]

Because the basic ocean predator algorithm has shortcomings such as low solution accuracy and poor stability at runtime, this paper proposes an elite reverse learning golden sine ocean predator algorithm. The elite reverse learning mechanism is added to improve the population quality of marine predators, and the comprehensive exploration scope of the algorithm is effectively expanded. The golden sine strategy is added to improve the way of hunting their prey of marine predators and reduce the search space of marine predators, and the performance of local development of the algorithm is effectively enhanced. The effectiveness of the improved strategy is evaluated by solving 12 benchmark functions and 12 CEC2017 functions. The solution test results show that the two strategies have an excellent effect on improving the performance of the ocean predator algorithm. [ABSTRACT FROM AUTHOR]

With the rapid accumulation of high-throughput data in biomedicine, it comes to be possible to break through the traditional drug design system and establish a rapid discovery method for antifungal drugs starting from rich data characteristics of biomedical information. High-throughput omics data are to calculate similar pharmacodynamic relationships between discovered drugs, which is applied into antifungal drugs discovery. Based on the CMAP and LINCS data platforms, we obtain the cell transcriptome data under the action of the compound as characteristic characterizations of cell's drug effects. Then we measure the similarity between the characterizations with GSEA method and WTCS algorithm. After that, we screen potential antifungal drugs by the comprehensive rank of the similarity of the drugs to be screened and known antifungal drugs. Based on the large-scale calculations of existing antifungal drugs, we found that drugs such as prenylamine and iri-notecan are expected to become antifungal drug candidates, and some of them are supported by related studies, which need to be verified by further experiments. This paper applies biological big data to quick drug rational design and provides important calculation methods for rational design of antifungal drug repositioning, and inspires and accelerates the development of existing antifungal drugs. [ABSTRACT FROM AUTHOR]

Aiming at the problems of the local chromatic aberrations, boundary artifacts and detail defects in inpainting facial images with large damaged areas, this paper proposes a facial image inpainting model based on partial convolution and multi-scale feature integration. The model is divided into two components: a multi-scale inpainting network and a discriminator network. The inpainting network achieves feature extraction and integration of facial images by effectively fusing deep and shallow image features through a multi-level feature extraction module and a main branching module. Moreover, a joint loss function consisting of content loss, perceptual loss, style loss, total variance loss and adversarial loss is constructed for training a multi-scale inpainting network and enhancing visual consistency between generated images and real images through mutual adversarial with discriminator networks. Experimental results show that, under different mask ratio conditions, this model can generate images with reasonable texture structure and contextual semantic information, and perform better in qualitative and quantitative comparisons. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the industrial control system vulnerability risk assessment is simple and not closely related to the industrial control environment, a multi-dimensional attribute analysis method of industrial control system vulnerability is proposed. Firstly, a template for discriminating vulnerability attack effectiveness and risk category attributes is established, and multidimensional evaluation indicators for the degree of risk vulnerability are defined. Secondly, an automated prediction model of risk level based on ernieCat is proposed, which uses the fusion features of vulnerability text descriptions and the intrinsic evaluation attributes of vulnerabilities to predict the seriousness level, hazard level and exploitability level of industrial vulnerabilities. Besides, this paper combines device-level critical information of industrial control system with vulnerability-level risk situations, and establishes multi-dimensional quantitative evaluation indicators to quantitatively assess the risk hazard level for industrial control system vulnerabilities. Experimental results show that the ernieCat model is superior for predicting vulnerability risk level. [ABSTRACT FROM AUTHOR]

As an important supporting technology for applications such as knowledge base construction and information retrieval, entity disambiguation plays an important role in the field of Natural Language Processing (NLP). However, in the short text environment, it is difficult for entity disambiguation to extract sufficient context features for disambiguation. Aiming at the characteristics of short texts, this paper proposes a disambiguation method of graph models based on entity topic relations. This method uses TextRank algorithm to infer the topic of corpus constructed by knowledge base information, and uses the result of topic inference as the representation of relationship between entities. By combining the disambiguation score given by the semantic matching model based on BERT, the disambiguation network graph is constructed, and the final disambiguation result is obtained through search and sorting. The data set provided in the short text entity link task of CCKS2020 is used to evaluate the method. The experimental results show that the proposed method is better than other entity linking methods in entity disambiguation of short text, and can effectively solve the entity disambiguation problem of Chinese short text. [ABSTRACT FROM AUTHOR]

To accurately recognize and evaluate tennis actions, by combining computer vision with tennis related knowledge, this paper proposes a tennis action recognition and evaluation method based on PoseC3D. Firstly, a pose estimation model based on resnet-50 is used to detect human targets in tennis video and extract bone key points. Secondly, the PoseC3D model is trained through the video data set collected in the professional tennis court, so that it can classify the sub actions of tennis. Thirdly, the dynamic time warping algorithm is used to evaluate the classified actions. Finally, based on the collected video data set, a large number of experiments are carried out. The results show that the Top1 accuracy of the proposed tennis action recognition method based on PoseC3D can reach 90.8%. Compared with the methods based on graph convolution network, such as AGCN and ST-GCN, it has stronger generalization ability. Moreover, the proposed scoring algorithm based on dynamic time warping can give real-time and accurate evaluation scores for corresponding actions after action classification, reducing the work intensity of tennis teachers and effectively improving the quality of tennis teaching. [ABSTRACT FROM AUTHOR]

A RC charging time zero crossing invariant oscillator is designed to provide high precision and high stability clock signals insensitive to voltage and temperature. This paper analyzes and deduces the characteristics that the time of zero crossing voltage does not vary with the power supply voltage in the process of RC charging, and adopts the temperature compensation technology to ensure that the time of zero crossing voltage does not vary with the temperature to the maximum extent. The simulation results show that the oscillator can output 2 MHz stably, the frequency fluctuation from 2.5 V to 5.5 V is less than 1%, the temperature fluctuation from -40 ℃ to 125 ℃ is less than 1%, and the maximum current under PVT condition is less than 150 μA. [ABSTRACT FROM AUTHOR]

The k-dominated skyline algorithm weakens the domination relationship between data points and is more suitable for high-dimensional data. k-dominated skyline bodies are suitable for multiple users to query with the k-dominated skyline algorithm, but the existing solution algorithms need to be improved in terms of time efficiency and code scalability. Therefore, this paper proposes an optimization algorithm for solving k-dominated skyline bodies. This algorithm stores the candidate set and the intermediate set for each user separately, and stores the non-k-dominated skyline points in the candidate set to the intermediate set of the corresponding user in the order of appearance of data points in the two sets during the k-domination checking process, so that the next user can filter and use them, which can reduce the number of comparisons between data points, avoids double counting, and improve query efficiency. A multi-user k-dominated skyline body parallel solving algorithm is also proposed, which effectively reduces the comparison time of data points through the Apache Flink parallel processing framework. The theoretical study and experimental data show that the proposed algorithm is highly efficient and can handle the multi-user k-dominated skyline problem well. [ABSTRACT FROM AUTHOR]

The quality compensation for the watermarking method based on the singular matrix robustness can improve the invisibility of watermarks to some extent. To optimize the compensation parameters, the paper analyzes the embedding principle of the watermarking method, finds out the relationship between pixel modifications and singular matrix changes，and studies the variation rules of pixels before and after quality compensation, thereby obtaining the optimal compensation parameters. Based on the optimal parameters, the paper further studies the relationship between quality compensation and robustness, and determines these factors that affect the compensation effect. The final experiments verify the quality compensation effect, and the comparison with similar methods proves that the proposed quality compensation method can improve the performance of watermarking. [ABSTRACT FROM AUTHOR]

Alzheimers disease (AD) is an irreversible neuro degenerative brain disease and the most common dementia in the elderly. Manual classification of Alzheimer's magnetic resonance image (MRI) has problems delay classification and time-consuming classification. As the aging population becomes more and more serious accurately and quickly classify patients with AD has important research significance. This paper combines convolutional neural network (CNN) technology with MRI technology and designs a 3D-ResNet model for AD classification which achieves 98. 39% accuracy 96.74% sensitivity and 99. 99% specificity on the validation set and achieves 97. 43% accuracy 94. 92% sensitivity and 99. 99% specificity on the test set. The classification time of each patient is 0. 23 s. In addition，for the problem that the pathogenesis of AD is not yet clear，this paper uses Class Activation Mapping (CAM) technology to visualize the AD-related brain regions. [ABSTRACT FROM AUTHOR]

A large number of human behaviors occur on the Internet? which has become the most important virtual space corresponding to the real space. Social stratification research in the traditional virtual space is based on objective indicators such as the opportunity and ability of network information resources possession, but does not involves the specific behavior of users using network resources and the content and nature of information and other factors. This paper makes use of the big data of users' online behaviors provided by China Internet Network Information Center, investigates and analyzes the characteristics and differences of users' online behaviors of different levels in the virtual space from two aspects of online time and content. The study finds that different levels of users' time spent in the virtual space and their attention focuses are very different. Higher level users can make better use of network resources to work and shopping, and stay in the virtual space has a relatively stable time. However, lower level users spend a lot of attentions on leisure and entertainment applications, and the stay time is not stable. In addition, this paper uses the neural network model (W2V-BP) based on Word2vec to conduct social stratification recognition of users' online behavior data in the virtual space, and the recognition accuracy reaches 90. 22%, indicating that there are behavioral characteristics in the virtual space that can distinguish social stratification. [ABSTRACT FROM AUTHOR]

With the explosive growth of micro-learning resources, a large number of unprocessed fragmented text resources bring great inconvenience to learners. In order to help learners to find suitable contents from fragmented resources for personalized learning, it is necessary to cluster micro-learning resources in the form of text. Therefore, this paper attempts to apply an improved density peak algorithm to micro-learning unit text clustering. Aiming at the problems of high dimensional sparse vector space, insufficient global consistency, cutoff distance sensitivity, and supervised selection of density peak centers when the density peak algorithm perform clustering in its field, this paper proposes two ap-proaches based on Latent Semantic Analysis (LSA) model. Firstly, a new definition of local density is proposed according to clustering requirements, density sensitive distance is used as the clustering criteria, and the global consistency problem of clustering is solved by solving the problem of cutoff distance sensitivity. Secondly, outliers are found by linear fitting to automatically find the density peak centers in order to realize unsupervised selection problem of peak centers. Experimental results on real data sets of micro-learning units show that the proposal is more suitable for text clustering of micro-learning units than the original algorithm and other classical clustering algorithms. [ABSTRACT FROM AUTHOR]

The black hole pattern is a landmark achievement in the study of human moving patterns. However, the black hole pattern has limitations in the evolution modeling of human moving patterns. This paper proposes a black hole pattern with time evolution characteristics. The definition of the new pattern needs to meet the three requirements of group scale, spatial locality and time persistence. This paper proposes a dynamic spatial network model with time evolution characteristics. Based on this model, we define a new black hole pattern and propose a corresponding mining algorithm. In order to improve the efficiency of the pattern mining algorithm, we design a candidate pattern pruning algorithm based on spatiotemporal partitioning, which effectively reduces the searching cost of the mining algorithm in spatiotemporal dimension. Finally, experiments based on real data verify the effectiveness and efficiency of the proposed black hole pattern and mining algorithm. [ABSTRACT FROM AUTHOR]