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Online reviews play an important role in customer relationship management, product marketing and other aspects. Effectively using online reviews to analyze user satisfaction is crucial for enterprises to improve their services and products. The variable design of traditional satisfaction analysis methods often relies on expert advice and seldom considers the asymmetric influence of positive and negative attributes. To solve these problems, this paper utilizes opinion mining technology to explore the features of customers online reviews and calculate services quality scores. Besides, PRCA technology is adopted to quantify the positive and negative influences of service attributes, and classify service attributes to Kano categories. Then, the characteristics of the different brand customer satisfaction under different granularity are analyzed, and the priority order of different customers' attributes is given. Finally, this paper mines five common attributes from coffee reviews. The experimental results show that different attributes have asymmetric effects on satisfaction and the influencing factors of customer satisfaction under different granularity have different characteristics. The corresponding refined enterprise management strategy is given. [ABSTRACT FROM AUTHOR]

With the development of integrated circuits, the size of devices decreases, which leads to the increase of traps in MOSFET gate oxide layer. The generated low frequency noise, especially Random Telegraph Noise (RTN), becomes more and more obvious, which challenges the reliability of devices. When there are more than one trap in the device, the coupling effect between traps also has an important influence on the analysis of RTN signals. Therefore, it is particularly urgent to carry out research on RTN signal extraction technology in complex and multi-trap situations. At present, the existing RTN extraction technology consumes a lot of time when processing large amounts of data, and the degree of automation is not high. These problems need to be solved urgently. In this paper, based on the non-Gaussian property of RTN signal, an automatic detection method of RTN signal and an automatic detection method of trap number are proposed, which makes the extraction of RTN signal more accurate and efficient. In addition, this paper also proposes an adaptive model for important parameters in the iterative process, and realizes the iterative acceleration of most RTN signal extraction. Finally, the method is used to extract parameters of the measured RTN signal, and the influence of coupling effect on the parameters is analyzed. [ABSTRACT FROM AUTHOR]

Deep learning has achieved great success in extracting data features, especially when dealing with graph data with rich node-to-node relationships. People have designed a variety of graph neural networks by using graph filters in the frequency domain to perform graph convolution operations. These graph neural networks mainly focus on designing fixed filters or learning simple filters, which may result in the filters not being suitable for all graph data. To solve the above problem, this paper proposes a node classification model MGCN based on meta-learning and graph filters to improve the universality of the graph filter. The model uses meta-learning to learn a set of initialization weights for the filters of the graph convolutional neural network (GCN), and after fine-tuning the filter weights, the model can quickly adapt to new tasks. To prove the effectiveness of MGCN, extensive experiments were conducted on 6 baseline datasets, and the experimental results show that the proposed model can be applied to a wider range of graph data compared to traditional graph neural network models. [ABSTRACT FROM AUTHOR]

First-order logic automated theorem proving is an important research content in the fields of knowledge representation and automated reasoning. How to effectively select clauses to participate in deduction is a research hotspot to improve the capability and efficiency of automated reasoning. Based on the good de-duction characteristics of multi-clause dynamic deduction, this paper proposes a measure and calculation method of clause activity and complexity by analyzing the properties of the variable terms and the structure of function terms of clauses, which can effectively evaluate clauses with different term structures. Based on the clause evaluation method, a multi-clause dynamic deduction algorithm with fully synergized deduction of clauses is proposed, which can effectively optimize the multi-clause deduction search path. The multi-clause dynamic deduction algorithm is applied to the international top prover Eprover 2.6, and the 2021 international automated reasoning competition problems (FOF group) is used as the test object. Within the standard 300 seconds, Eprover 2.6 with the proposed multi-clause dynamic deduction algorithm proven four more theorems than the original Eprover 2.6, and the average proof time is decreases by 1.12 seconds under the condition of proving the same number of theorems as Eprover 2.6. In addition, it can prove 16 unproven theorems of Eprover 2.6, accounting for 15.1% of the total unproven theorems. The experimental results show that the proposed multi-clause dynamic deduction algorithm is an effective inference method, which can improve the capability and time efficiency of automated reasoning to a certain extent. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low efficiency and rough path planning due to the randomness and blindness of node expansion in Bi-directional rapidly-exploring random tree algorithm, this paper proposes a goal-directed Bi-RRT algorithm based on information inspiration. In order to reduce the randomness and blindness of node expansion, the tree node expansion method is optimized. The node information generated by regression analysis is used to optimize the extended node evaluation function to strengthen the target tropism of node growth, and the expansion direction is constrained by node and environmental cost. The redundant nodes in the initial path are eliminated by branch and bound method, and the path satisfying the maximum steering Angle constraint is obtained. The cubic B-spline curve is used to smooth the path to improve the smoothness and continuity of the path. Finally, the proposed algorithm is compared with other classical algorithms in different environments based on the MATLAB simulation platform, and the experimental results verify the effectiveness and enforceability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In the field of music information retrieval (MIR), classification according to music genres is a challenging task. Traditional audio feature engineering methods requires manually selecting and extracting music signal features for processing, resulting in complex feature extraction process, unstable model performance and poor generalization. The method combining deep learning with spectrogram also has some problems such as unsuitable model for some data and difficulty in global feature extraction. This paper proposes a music genre classification model based on convolutional attention mechanism, called MGTN. MGTN combines two music genre classification methods: input spectrogram and audio signal feature extraction, to construct audio time series data, which greatly improves the model's ability to extract features and generalization, and provides a new idea for music genre classification. Experimental results on GTZAN and Ballroom datasets show that the MGTN model can effectively fuse input data from two different modalities. Compared with dozens of benchmark models, the MGTN model has strong advantages. [ABSTRACT FROM AUTHOR]

In the driving scenario, due to the occlusion between pedestrians and their scale variations, detection model have low accuracy, high model parameters, and difficulty in deploying to mobile terminals. This paper proposes a lightweight real-time pedestrian detection model, LPD-YOLO, based on the YOLOv5s model. Firstly, in the feature extraction part, the original backbone network is replaced with MES Net (Mish-Enhanced Shuffle Net), and an attention module SA (Shuffle Attention) is embedded in the backbone network to enhance network feature extraction ability. Secondly, in the feature fusion part, the original PANet is improved by using the DS-ASFF structure to fully fuse feature maps of different sizes. Then, standard convolution is replaced with GS convolution in the feature-covergent network part without affecting accuracy, further reducing model parameters and computation. Finally, in the prediction part, the original loss function is improved by using the OTA label assignment strategy combined with α-IOU to accelerate model convergence. Experimental data show that compared with YOLOv5s, LPD-YOLO has 81.2% fewer parameters, 46.3% lower floating-point operation volume, 75.8% smaller model size, and 3.3% higher detection accuracy. The single image detection speed is 13.2 ms, which better meets the real-time detection requirements of dense pedestrians in driving scenarios. [ABSTRACT FROM AUTHOR]

Text-to-image generation aims to generate realistic images from natural language descriptions, and is a cross-modal analysis task involving text and images. In view of the fact that the generative confrontation network has the advantages of realistic image generation and high efficiency, it has become the mainstream model for text generation image tasks. However, the current methods often divide text features into word-level and sentence-level training separately, and the text information is not fully utilized, which easily leads to the problem that the generated image does not match the text. In response to this problem, this paper proposes an image confrontation cascade generation model (Union-GAN) that couples word-level and sentence-level text features, and introduces a text-image joint perception module (Union-Block) in each image generation stage. By combining channel affine transformation and cross-modal attention, it fully utilizes the word-level semantic and overall semantic information of the text to generate images that not only match the text semantic description but also maintain clear structures. Meanwhile, jointly optimizing the discriminator and adding spatial attention to the corresponding discriminator allows the supervisory signal from the text to prompt the generator to generate more relevant images. Compared with multiple current representative networks such as AttnGAN on the CUB-200-2011 dataset, experimental results show that the FID score of our Union-GAN is 13.67, an increase of 42.9% compared to AttnGAN, and the IS score is 4.52, an increase of 0.16. [ABSTRACT FROM AUTHOR]

Full-link stress test, as an emerging software testing technique, performs stress tests in the production system directly. It aims at evaluating the performance of online systems accurately. Traffic-split techniques based on shadow databases can guarantee the production data not to be polluted during the process of full-link stress test. Based on SQL engine, this paper designs and implements a complete open-source traffic-split system, named ShadowDB, for full-link stress test. The main idea of ShadowDB is to split the traffic of user requests through a SQL parser and a SQL router. Currently, ShadowDB can correctly distribute all SQL statements of six different kinds of relational database management systems. Furthermore, it can support two traffic-split algorithms, i.e., column-based shadow algorithm and hint-based shadow algorithm. ShadowDB implements all of the interfaces of JDBC, enabling online systems to incorporate it without any change. ShadowDB can be embedded in the application programs, thus it do not forward the requests through networks, which has the minimal impact on the request efficiency and guarantees the reliability of full-link stress test. Extensive experiments wire conducted based on two widely-used benchmarking tools. The experimental results show that ShadowDB performs much better than the comparing systems. [ABSTRACT FROM AUTHOR]

Aiming at improving the performance of the default loop scheduling and tile size of Pluto, a commonly used polyhedral compiler, this paper proposes a method to compute a variety of legal permutations for its default scheduling and auto-tune its performance according to the configuration space composed of permutations and tile sizes. Through the processing of scalar dimension that defines loop fusion, both intra and inter permutations for imperfect loop nest are realized. Four machine learning driven auto-tuning strategies are proposed to find the optimized combination of permutation order and tile size for a loop with a given problem size. Under the default tile size, the optimal permutation generated by the extended Pluto compiler in a parallel environment achieves a maximum speedup of 4.02 and a geometric mean of 2.12 compared with the default scheduling of Pluto. By further searching for a better combination of permutation order and tile size, the best auto-tuning strategy achieves a maximum speedup of 5.48 and a geometric mean of 2.86 compared with Pluto's default optimization in a parallel environment. In addition, the best configuration and the learned model obtained by auto-tuning for a particular problem size, when being applied to similar problem sizes, also outperform the default optimization of Pluto in various degrees. [ABSTRACT FROM AUTHOR]

Graph Convolutional Networks (GCN), an algorithm for processing non-Euclidean data, is currently mainly implemented on deep learning frameworks such as PyTorch for GPU acceleration. GCN's computation process involves nested matrix multiplication and data access operations, which can be satisfied by GPU in real-time but have high deployment costs and low energy efficiency. To improve the computational performance of GCN algorithm while maintaining software flexibility, this paper proposes a custom GCN accelerator based on RSIC-V SoC, which extends the dot product operation and hardware accelerator through hardware-software co-design in the hummingbird E203 SoC platform. The neural network parameter analysis determines the hardware quantization scheme from floating point to 32-bit fixed point. Experimental results show that the proposed accelerator has no accuracy loss and can achieve a maximum speedup of 6.88 times when running GCN algorithm on Cora dataset. [ABSTRACT FROM AUTHOR]

The router is a key component of the high performance interconnect network, which can flexibly build a topology with low network diameter, rich routing path and high fault tolerance performance. The hierarchical structure divides the entire router into multiple small subcrossbars. The typical number of sub-crossbar switches is equal to the number of router ports, and each sub-crossbar switch corresponds to an input and output port. The input and output of each subcrossbar are equipped with buffers, resulting in a large number of buffers inside the hierarchical router that limits the scalability. The network structure will be used to build the network topology of the system in a chip, such as connecting smaller switches through a grid, a full interconnect, or a fat tree implemented in a router through integrated circuit technology, which externally appears as a high radix router. Network structures have low costs and require consideration of not only the system network topology's performance after building the system network but also the router's own routing issues. This paper proposes a hierarchical structure router based on Clos network, combining the advantages of high performance and low cost of traditional hierarchical structure, and proposes two schedule algorithms for Clos network. In uniform traffic mode, they approach 100% bandwidth utilization, and the RTL synthesis evaluation achieves a maximum area saving of 25.9%. [ABSTRACT FROM AUTHOR]

In this paper, according to the latest China HPC TOP100 rank list released by CCF TCHPC in the late November, the total performance trends of China HPC TOP100 and TOP10 of 2023 are presented. Followed with this, characteristics of the performance, manufacturer, and application area are analyzed separately in detail. [ABSTRACT FROM AUTHOR]

Equilibrium Optimizer (EO) is a recently proposed excellent metaheuristic algorithm, but it encounters issues such as insufficient search ability, poor operability, and low search efficiency when solving complex optimization problems. Therefore, this paper proposes an improved EO, namely Differential mutation and Territorial search EO (DTEO). Firstly, a differential mutation method with territorial search is proposed to update the concentration of the best particle. Then, an elite-worst individual particle differential mutation strategy is proposed to strengthen the worst individual. Finally, a differential mutation strategy with information sharing and a simplified concentration updating way in EO are proposed and integrated dynamically to update the other particles' centralizations to improve the operability and search ability of the algorithm and reduce the running time. The experimental results on the complex functions from CEC2014 test set demonstrate that compared with EO and other excellent algorithms, DTEO has stronger search ability, higher efficiency, and stronger operability. Experimental results on robot path planning also show DTEO is more competitive. [ABSTRACT FROM AUTHOR]

Recommendation plays an important role in satisfying users􀆳 needs of information and solving information overload. Heterogeneous information network contains rich semantics and provides a new way for recommendation optimization. Based on the research of recommendation on heterogeneous information network at home and abroad, this paper conducts bibliometric analysis and visual analysis with SATI, Ucinet, NetDraw, and SPSS to obtain the current research focuses and progress. According to the clustering results of literature keywords, the previous research are mainly based on clustering, random walk, meta-path, matrix factorization, network embedding algorithms, and applied in academic research, points of interest, Web services, social friends, patent trading, news and other recommendation scenarios. There is still a large development space for recommendation research based on heterogeneous information network. Future research can be carried out in dynamic recommendation, deep network representation learning and wider applications. [ABSTRACT FROM AUTHOR]

QUIC, as an emerging transmission protocol parallel to TCP, follows the TCP research approach. The mainstream research way is hardware offloading, which offloads computation-intensive functional modules to network devices and replaces host CPU computation by hardware processing. However, due to the poor generality of hardware offloading, although its performance is high, it cannot guarantee user programmability. To overcome this limitation, this paper proposes a software offloading model--NanoBPF, which is a protocol offloading model based on the RISC-style many-core DPU (Data Processing Unit). By modifying the Bootloader's startup code, it guides the eBPF (extended Berkeley Packet Filter) code as a runtime environment and offloads encryption and decryption functional modules with high CPU utilization rates in the protocol stack using software. The encryption and decryption functional modules are written in high-level languages (C) and compiled into custom BPF (Berkeley Packet Filter) bytecode dynamically loaded into the DPU. The throughput and fairness of the prototype system are validated using local and Docker-based network topologies. The results show that software offloading of message encryption and decryption can increase the message throughput of the protocol stack by nearly 13%, and under certain conditions, it can ensure link fairness with TCP. [ABSTRACT FROM AUTHOR]

Satellite network has attracted extensive attention from academia and industry due to its characteristics of wide coverage, high bandwidth, strong survivability, natural broadcast and so on. However, due to the characteristics of long propagation delay, high bit error rate and asymmetric bandwidth, the traditional TCP protocol has poor performance in satellite network. In recent years, with the rapid development and large-scale deployment of the giant constellations represented by "Starlink", the satellite network has shown the characteristics of high dynamics, which poses a severe challenge to the high-performance transmission of satellite constellation network. This paper summarizes the research progress of new technologies for satellite network transport protocol optimization that have emerged in recent years. Especially, the new technologies represented by multi-path transmission, QUIC, intelligent transmission optimization are deeply compared and analyzed. [ABSTRACT FROM AUTHOR]

In order to maintain the size of the learned clause database and perform unit propagation with reasonable cost during the SAT solver􀆳s solving process, it is necessary to evaluate the learnt clauses and remove those that are not useful to the solving process. Therefore, it is necessary to propose a new method for evaluating clause usefulness, including the analysis and deletion of learned clauses, for dynamic management strategies of the learned clause database, thereby retaining the clauses that are most effective for solving and improving solving efficiency. This paper starts by capturing the recent polarity assignments of learnt clauses, combined with a heuristic based on literal polarity commonly used in the backtracking process of modern solvers-progress saving, to infer the relevance of a given learnt clause to the remaining search steps. Based on the two state-of-the-art Conflict Driven Clause Learning (CDCL) solvers, Glucose and MapleLCMDistChronoBT, their clause evaluation algorithms are improved and tested. The experimental results show that this clause evaluation strategy based on the recent literal polarity assignment can generally improve the solving efficiency of CDCL serial and parallel solvers, and effectively reducing the excessive time consumption of original solvers on some problems. Besides, 2 more Conjunctive Normal Form (CNF) files are solved at the level of advanced solvers, and the average solve time of a single file is decreased by 13~34 seconds. [ABSTRACT FROM AUTHOR]

In the context of the rapid development of network information, the demand of different music lovers for music information retrieval is also increasing, and music classification has become an important research subject. This paper proposes a fast classification method of DUPSO-DSVM folk songs, which combines dissipative uniform particle swarm optimization (DUPSO) with distance sorted SVM (DSVM). This method uses DUPSO algorithm to optimize the penalty coefficient C and kernel function parameter g of SVM, and uses DSVM algorithm to optimize the parameter optimization time of DUPSO algorithm. The experimental results show that, DUPSO-SVM algorithm has a classification accuracy of 84%. After using DUPSO-DSVM algorithm, the training time of the algorithm only accounts for 26.26% of the unused DUPSO-DSVM algorithm, but it still maintains a high classification accuracy. [ABSTRACT FROM AUTHOR]

Aspect-level sentiment classification aims to discriminate the sentiment polarity of a specific aspect in a sentence. Although attention-based recurrent neural network models perform well among existing solutions, they are not ideal for processing "semantically ambiguous" sentences that are short and contain many neologisms and polysemous words. Therefore, this paper proposes a neural network model based on knowledge graph and attention mechanism. The basic idea is to use a knowledge base to obtain a relevant concept set of aspect words and integrate external information to enhance the semantic representation of the text. Firstly, the output of bidirectional long short-term memory network is combined with self-attention mechanism to generate context representation. Then, the upper and lower context representations are combined to use dual attention to obtain external knowledge from the knowledge graph and obtain knowledge vectors related to aspect words. Finally, the two parts of content are input together into a fully connected network to calculate the aspect-level sentiment tendency. Experimental results show that compared with other models, the proposed model significantly improves classification performance. [ABSTRACT FROM AUTHOR]

To address the problems of multi-stage encoding of the encoder of the graph-to-sequence question generation model and the easy loss of rich sequence information and semantic structure information in the paragraphs during decoding, this paper designs a model based on multi-stage timing and semantic information enhancement(MS-SIE). The model first fuses the semantic information of the passages encoded at different stages of the encoder and inputs them to the recurrent neural network for encoding. Then, an iterative graph neural network is introduced in the decoding stage to combine the encoded paragraph information with the rich semantic structure information hidden in the previously generated text questions in the decoding stage. Finally, a recurrent neural network based on an attention mechanism is used to generate the questions. The results show that the model proposed significantly outperforms the existing sequence-to-sequence model and graph-to-sequence model in both automatic evaluation metrics and manual evaluation metrics. [ABSTRACT FROM AUTHOR]

Integrated circuits are an important part of electronic products, and their quality control and fault analysis are prerequisites for the long-term operation of electronic products. Scanning Acoustic Microscope (SAM), as a non-destructive defect detection method, has been widely used in imaging detection and internal defect identification of integrated circuits. In response to the intelligent demand for acoustic scanning image defect detection and the requirements for real-time and accurate detection, this paper develops a software for integrated circuit acoustic scanning image defect detection based on machine vision, providing integrated functions for image processing and image detection. The algorithm framework of this software combines deep learning technology, traditional image processing technology using OpenCV, and JavaScript interface design technology, allowing it to manage various types of integrated circuit data and analyze, process, and determine defects in scanning acoustic images. [ABSTRACT FROM AUTHOR]

With the development of deep learning, steganography based on text generation has made significant break-throughs. Existing text-based steganography methods suffer from exposure bias, where the input during training comes from real sample labels, while the input during prediction comes from the output predicted in the previous time step. This difference in input samples between training and prediction leads to error accumulation, resulting in a large distribution difference between generated samples and real samples. To address this problem, this paper proposes a text steganography model called TS-GANMA based on generative adversarial networks and multi-head attention. First, a text generator is trained using a generative adversarial network, and multi-head attention mechanisms are used to extract multi-head attention scores to participate in the reward calculation of the reward module, obtaining feedback information more suitable for the generator. Then, the generator and discriminator are trained in an adversarial manner, which can solve the exposure bias problem and optimize the text generation model. Finally, the conditional probability distribution output by the text generation model is encoded to embed secret information. The experimental results demonstrate that the steganography method based on TS-GANMA has a much lower perplexity than the methods based on LSTM-vlc and ADG at the same embedding rate. This is because the steganographic text generated by the TS-GANMA model fits the statistical distribution of the real text better, and can generate higher quality steganographic text. [ABSTRACT FROM AUTHOR]

Aiming at the access structure of attribute-based encryption, this paper proposes an attribute-based encryption scheme that supports complex access policies. The scheme uses reduced ordered binary decision diagrams (ROBDDs) as the access structure, where a user's attribute set corresponds to a path in the ROBDD. The ROBDD can not only represent any Boolean function about attributes, but also reduce valid paths by simplifying nodes in the access structure, thereby preventing interference from irrelevant attributes and reducing the computational cost in the encryption phase. By integrating effective path feature values into Boolean functions, the ciphertext does not need to store multiple effective path feature values in complex access policies, reducing the storage cost of the ciphertext. The scheme outsources attribute authentication computation to the decryption server, reducing the local computation cost of users in the decryption phase, and uses group element exponentiation instead of bilinear pairing to reduce the computational cost of the scheme. The security model proves that the scheme is IND-CPA secure, and performance analysis and experimental simulation show that the proposed scheme has lower computational and storage costs. [ABSTRACT FROM AUTHOR]

Mandatory access control (MAC) for operating system (OS) brings strong security guarantee for the system because it runs at high privilege level. However, the classical OS MAC only supports static security policies. When the security requirements change, the security policies must be reconfigured and reloaded. Therefore, it is difficult to meet the requirements of dynamic regulation of access permissions in scenarios such as high-sensitivity application state transition, cloud native dynamic scheduling, and BYOD. Attributes-based access control has strong extensibility, flexibility and expression ability, which provides a solution to improve the dynamic and flexibility of the security policy of MAC in OS. In this paper, the theoretical model and system architecture model of attributes-based dynamic mandatory access control for operating systems are proposed. Then, the prototype system is designed and implemented by combining with the classic MAC mechanism of Linux, and the feasibility of the model is verified. Finally, in view of the possible performance impact of the introduction of attribute factors, the optimization research of access control is carried out from two aspects of time and space. [ABSTRACT FROM AUTHOR]

In electronic systems, the problem faced by high-speed circuit design ultimately lies in noise. In the actual high-speed circuit design and production process, there are many factors that can cause problems with signal integrity and power integrity, such as deviations between simulation models and actual PCBs, unreasonable designs, and errors generated during PCB board manufacturing. To address these issues, we conduct research on the analysis and optimization of power switch noise coupling interference on high-speed signals. We first introduce the theory of signal integrity and power integrity, and then conduct theoretical analysis, oscilloscope testing analysis, Cadence simulation analysis, and optimization verification on a case where power switch noise coupling interference affects high-speed signals in a motherboard design process. This paper provides a systematic analysis and solution for similar problems. [ABSTRACT FROM AUTHOR]

Precipitation nowcasting refers to the high-resolution prediction of precipitation in the short term, which is an important but difficult task. In the context of deep learning, it is viewed as a radar echo map-based spatiotemporal sequence prediction problem. Precipitation prediction is a complex self-supervised task. Since the motion always changes significantly in both spatial and temporal dimensions, it is difficult for ordinary models to cope with complex nonlinear spatiotemporal transformations, resulting in blurred predictions. Therefore, how to further improve the model prediction performance and reduce ambiguity is a key focus of research in this field. Currently, the research on precipitation nowcasting is still in the early stage, and there is a lack of systematic classification and discussion about the existing research work. Therefore, it is necessary to conduct a comprehensive investigation in this field. This paper comprehensively summarizes and analyzes the relevant knowledge in the field of precipitation nowcasting from different dimensions, and gives future research directions. The specific contents are as follows: (1) The significance of precipitation nowcasting, and the advantages and disadvantages of traditional forecasting models are clarified. (2) The mathematical definition of the nowcasting prob-lem is given. (3) Common predictive models are comprehensively summarized, analyzed. (4) Several open source radar datasets in different countries and regions are introduced, and download links are given. (5) The metrics used for prediction quality assessment are briefly introduced. (6) The different loss functions used in different models is discussed. (7) The research direction of precipitation nowcasting in the future is pointed out. [ABSTRACT FROM AUTHOR]

Satellite communication is widely used in fields such as encrypted communication and emergency communication. Its baseband processing algorithm is relatively complex and requires strong computing power support. Traditional FPGA and DSP platforms have a long development cycle, while GPU-based software-defined radio (SDR) solutions are convenient and have excellent performance. This paper proposes a group of baseband algorithms for satellite communication based on GPU, which realizes high-speed processing of the satellite communication downlink. Experimental results show that the GPU-based satellite communication link meets the lowest latency requirements, and the maximum baseband processing speed reaches 978Mbps. [ABSTRACT FROM AUTHOR]

The asymmetric prediction error histogram algorithm can generate two asymmetric histograms that are left by the value of 0 and right by the value of 0. In the embedding process, the two histograms move in opposite directions, so that some modified pixels can be restored to the original pixels, that is, compensation and original reaction will occur. In view of this feature, the algorithm in this paper combines the multi-pair histogram translation algorithm to generate two pairs of asymmetric histograms. Through two rounds of asymmetric histogram embedding, i.e., four layers of data embedding, the probability of compensation restoration of modified pixels is increased. In addition, by studying the change range of peak points and pixel values of the original image after data embedding, all image blocks are classified, and the image blocks with high embedding probability are preferentially selected to reduce unnecessary pixel modification. Compared with other algorithms, this paper makes full use of the compensation and restoration reaction of the asymmetric histogram algorithm, combines the characteristics of the image pixel value distribution to reduce the invalid modification of pixels, and better ensures the quality of the dense image. [ABSTRACT FROM AUTHOR]

Quantum game theory is an interdisciplinary area combining quantum information and game theory. Theoretical studies demonstrate that quantum games surpass the maximal interest of classical ones, which can be applied to analyze and solve fundamental problems in various fields, such as quantum communication and quantum computing. Aiming at a Bayesian quantum game model with conflictive interests, this paper proposes a programmable photonic quantum chip structure, and completes the quantum game experiment with silicon based optical quantum chip for the first time. By dynamically generating and modulating entangled states on the chip, the experiment demonstrates the advantage of the quantum game over the classical one. This paper shows that photonic quantum chips play a vital role in the research of quantum game theory and provide a promising methodology to study more complicated problems in the field of quantum information. [ABSTRACT FROM AUTHOR]

Automatically extracting useful information from electronic medical records (EMRs) and assisting in disease diagnosis has important theoretical and practical significance for clinical decision support and smart hospital construction. However, there is an imbalanced distribution of symptom data in EMRs, which leads to insufficient data volume for some diseases in assisted diagnosis. Moreover, traditional methods ignore the heterogeneity and multi-source contextual information of medical records, which can lead to poor disease prediction accuracy. This paper proposes a clinical assisted diagnosis prediction model HCAD based on heterogeneous graph medical record attention network. Firstly, the problem of imbalanced electronic medical record data is solved by constructing an external medical knowledge graph. Secondly, by effectively integrating patient condition descriptions and physiological records and designing node-level attention mechanisms and semantic relationship-level attention mechanisms, the importance of node and different semantic relationship information is identified. Finally, highly representative patient node vector representations are obtained through hierarchical aggregation, which accurately predicts diseases. Experiments on a real EMR dataset show that the proposed model has high feasibility, effectiveness, and interpretability, with an average F1 value improvement of 7.45% compared to the baseline. [ABSTRACT FROM AUTHOR]

To address the problems of low convergence accuracy and easy local optima trapping in the standard Equilibrium Optimizer (EO) algorithm, this paper proposes an Improved Opposition-based learning Equilibrium Optimizer Algorithm based on Neighborhood Searching (IOLEONS) that combines neighborhood topology search. Firstly, the hyperbolic tangent adaptive operator is used to modify the average concentration value in the balance pool to improve the convergence accuracy of the algorithm. Then, the Euclidean distance between particles is calculated, and a neighborhood search mechanism is introduced to further enhance the algorithm's local development ability, better balancing the algorithm's development and exploration stages. Finally, the dynamic symmetric opposite learning strategy with Chebyshev mapping is used to enhance the population's disturbance ability, improve the diversity of the population, and help the population escape from local optima. The convergence of the improved algorithm is analyzed, and eight benchmark test functions are selected in the simulation experiments. The results of Wilcoxon signed-rank test and Friedman rank test show that the improved algorithm has better optimization performance. [ABSTRACT FROM AUTHOR]

Face liveness detection is a key factor in ensuring the security of face recognition systems. In particular, disentangled learning methods can effectively address the problem of generalizing datasets in face liveness detection. However, existing disentangled learning methods often take the entire face image as input and parse out forged trace elements, ignoring the issue of local details of forged traces. To address this issue, this paper improves the existing forgery trace disentanglement network and proposes a multi-adversarial discriminative network model. The discriminator is designed with a primary discriminator and a regional discriminator. A facial mask module is introduced to generate facial skin and feature masks. Local facial information is integrated to make the generated images more closely resemble the distribution of face images in the dataset, while also disentangling an enhanced version of the forgery trace. The proposed multi-adversarial discriminative network effectively enhances the effect of forgery trace on forged face images and improves the accuracy of face liveness detection. Specifically, the detection error rate of our model on the OULU-NPU dataset in two experiments is only 0.8% and 1.4%, significantly lower than that of the STDN. At the same time, good detection results are achieved on the Idiap Replay-Attack dataset. To verify the transferability of our method, cross-domain experiments on the NUAA dataset and the Idiap Replay-Attack dataset also achieves good results. [ABSTRACT FROM AUTHOR]

Domain name service (DNS) plays an important role in the Internet. The combination of ENUM technology and DNS has been widely used in the three networks convergence, and the BIND system accounts for a high rate in the DNS. This paper compares the difference between the RFC and the BIND system on the longest matching rule. By comparing the results of the two types of DNS, a dual search strategy is proposed to modify the BIND system, so that the DNS conforms to the standard RFC. This can provide ideas for more standard service customization processing, and deploy an application in the real network. [ABSTRACT FROM AUTHOR]

Event detection mainly studies trigger word detection and event type recognition. At present, most models based on deep learning focus on semantic role information, syntactic dependency tree information and pre-training models, but ignore the importance of parts of speech. To solve this problem, this paper proposes a Chinese event detection method based on nugget proposal network with part-of-speech attention mechanism. The method firstly obtains part-of-speech sequence based on NLP part-of-speech tagging tool, then uses the CBOW algorithm to obtain part-of-speech embedding, and finally uses part-of-speech embedding in the model to calculate part-of-speech attention for event detection. Experiments on the ACE2005 show that the F1 score of the model with part-of-speech attention is improved by 3.8% and 2.4% respectively on the event detection task, which proves the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Aiming at the problem of large number of parameters and low detection efficiency of the traditional target detection method in defect detection of electronic components, this paper proposes a target detection method based on the lightweight YOLOX detection network. Firstly, the backbone network is lightened using deeply separable convolution to reduce parameters and improve detection speed. Secondly, a spatial Pyramid-based channel attention model is constructed to filter and fuse features of different scales to enhance the feature weights of small size defects. In the feature fusion upsampling process, efficient channel attention is added to improve detection accuracy with slightly increased parameters. Finally, the EIoU loss function is used to optimize the IoU loss function, and the cosine annealing algorithm is used to make the model achieve the best detection effect. The model is tested on a self-made dataset of electronic component appearance defects, and the average detection accuracy reaches 98.96%, with a detection time of approximately 0.09 seconds per image. Compared with the original model, the detection speed is doubled and the model size is reduced by about 60%. The model is also validated on the PCB defect public dataset, achieving fast detection of target defects. [ABSTRACT FROM AUTHOR]

The continuous upgrading and development of small-angle X-ray scattering (SAXS) equipment have generated more high-dimensional scattering data, which poses great challenges for researchers to quickly obtain experimental results. Researchers urgently need effective automated classification methods to speed up data representation and obtain higher accuracy. However, many models learn features mainly for illumination images, ignoring the characteristics of scattering images and resulting in lower classification accuracy. Therefore, based on the characteristics of scattering patterns, this paper proposes a bimodal fine-grained feature extraction model called BRTNet. The model adopts a bimodal input mode. The first mode is the feature learning network PRS using a multi-scale convolution architecture, which learns the micro-information of scattering images. The second mode is the multi-head attention mechanism ConvTransformer fusing local information, which learns the correlation information of scattering sequences. Then, the model combines image information and sequence information, fuses the dual-branch features, classifies the scattering data, and obtains the classification results. Experimental results on the biological solution scattering dataset show that the model's classification accuracy exceeds 89%, which has a significant advantage over the baseline model. [ABSTRACT FROM AUTHOR]

Edge computing is one of the important technologies for the future Internet. Due to the massive dynamic and heterogeneous edge devices in the edge network, some of these edge devices may be malicious. Malicious edge devices will disrupt data processing and transmission within the edge network. With the increasing number of edge devices, edge computing faces a serious trust demand. Therefore, this paper conducts an in-depth investigation of the trust issue in edge computing. Firstly, we review and classify the current research from the perspectives of edge trust management and edge trust evaluation. Then, we discuss and analyze the trust management system based on blockchain and the trust evaluation methods. Furthermore, we also analyze and discus the path planning of trusted data collection, trust incentive mechanism, collaborative trust evaluation, and the application of edge computing trust in the edge trust issue. Finally, according to the completed review and evaluation, we explore some challenges of trust problems in edge computing and suggest the directions that are worth our research effort in the future. [ABSTRACT FROM AUTHOR]

The current main trajectory similarity connection methods are based on GPS trajectories. Optimization methods for GPS trajectories cannot be directly applied to the problem of connecting subway passenger trajectories. By fully utilizing the spatiotemporal characteristics of subway passenger trajectories and leveraging the trajectory􀆳s repetitiveness and symmetry, the trajectory is transformed from a point sequence to an OD sequence to reduce the trajectory length and save storage space. This paper focuses on the design and implementation of the trajectory connection algorithm based on PPJoin+ on the Spark platform. The method is validated on a 13-node Spark cluster and a large-scale dataset containing 5 million passenger trajectories (560 million tap records collected in two consecutive months). The experimental results show that the PPJoin+ algorithm based on OD sequence only takes 14.0 minutes, which saves 62.5% of the execution time compared to the default point sequence trajectory connection algorithm and 78.2% of the execution time compared to the Dima connection algorithm, and exhibits good scalability. [ABSTRACT FROM AUTHOR]

Networks-on-Chip (NoC) has become the standard paradigm for interconnect networks in multi-core processors. However, as the power supply voltage gradually decreases and the process size is reduced, the probability of soft errors in NoC increases. Error correction codes are commonly used in NoC router designs to tolerate soft errors. However, traditional router designs often only use Hamming codes for error correction, which has the problem of insufficient error correction capability, despite its simple design structure. This paper proposes a hybrid-hardening NoC router design based on error correction codes. The core idea of this design is to adopt different fault-tolerant code designs based on the importance of information bits, thus achieving a balance between router reliability and fault-tolerant overhead. Experimental results show that our design improves system reliability compared to the baseline design under synthetic traffic and PARSEC benchmark, and the hardware synthesis results also show that this design can shorten the critical path delay by 4%. [ABSTRACT FROM AUTHOR]

As the scale of integrated circuit design continues to increase, verification has become one of the bottlenecks in the design process. Currently, simulation is still one of the dominant methods for integrated circuit design verification, and the completeness of simulation is usually measured by various coverage metrics. Functional coverage is a higher level of coverage, and in practical engineering, functions are often presented in the form of SystemVerilog assertions. Currently, it is difficult to generate a large number of test vectors that activate assertions using commonly used random test vector generation methods. When using constraint solving strategies, if the coverage condition involves non-initial input signals (internal signals, output signals), the efficiency of constraint solving will be extremely low, making it still difficult to cover the target assertion. To address the coverage problem of assertions containing non-initial input signals, this paper proposes a Surrogate model-based assertion coverage improvement method, which mainly generates a Surrogate model that reflects the relationship between non-initial input signals and initial input signals and only contains initial input signals, and then uses this Surrogate model as the object of constraint solving, thus reducing the complexity of constraint solving. Experimental results show that this method has a significant improvement in assertion coverage compared to random test vector generation. [ABSTRACT FROM AUTHOR]

Air pollution seriously affects human health and social sustainable development. However, the multi-variable air quality data obtained by sensors often have missing values, which brings difficulties to data analysis and processing. Currently, many analysis methods for changes in a certain air component only rely on time data and spatial data of this attribute, ignoring the influence of other air components on the trend of this attribute in the same time interval. In addition, it is difficult to achieve ideal results in filling discrete missing data. This paper proposes a Time Attention Model (TAM) based on deep learning, which uses attention mechanism to focus on the correlation between different timestamps and the correlation between different feature time series, and combines short-term historical data to fill missing values in multi-variable air quality data. The proposed model is evaluated using air quality data from Beijing, and the experimental results show that TAM has advantages over ten other baseline models. [ABSTRACT FROM AUTHOR]

Changes in the instruction set architecture will result in changes in the processor hardware platform, and binary applications compiled for the old hardware platform will not be able to continue running on the new hardware platform. In this paper, a hybrid instruction set architecture compatible with multiple instruction sets at application level is proposed, and the processor based on the hybrid instruction set architecture can natively run multiple instruction set applications, which can effectively avoid the repetitive work of program developing and porting or the performance loss of the binary translation execution. Based on a self-developed processor, a hybrid instruction set processor compatible with RISC-V at application level is implemented. Compared with a single instruction set, the hardware overhead of supporting for two instruction sets at application level is only increased by 0.45%. The FPGA prototype system successfully boots the operating system ported to the hybrid instruction set architecture, and can correctly run the application based on each instruction set, which verifies the feasibility of the hybrid instruction set architecture idea. Under RISC-V instruction set, the processor achieves 5.58/MHz for Coremark, 8.44/GHz for SPECint2006 and 10.75/GHz for SPECfp2006. [ABSTRACT FROM AUTHOR]

Standard cell library is the foundation of chip design, analysis, and verification, and its generation requires a lot of time and server resources. Therefore, vendors often only provide standard cell libraries under a few corners. However, the design of chip performance, power consumption, and reliability requires delay information of standard cells under multiple voltages, temperatures, and parameters (such as drive strength, channel length, and threshold voltage). To quickly and accurately calculate the delay of standard cells under multiple corners, this paper proposes a machine learning-based method for multi-voltage, multi-temperature, and multi-parameter standard cell delay calculation. By studying the factors that affect the delay of standard cells in depth, data sets are extracted from the 28nm process standard cell library and timing reports. Machine learning algorithms are used to train and calibrate the standard cell delay calculation model. The establishment of the model takes only a few minutes, which is much less than the time consumed by simulation methods (usually hundreds of hours). The average calculation error of the model is 1.542 ps for unknown voltage cell delay, 1.814 ps for unknown temperature cell delay, and 2.202 ps for cell delay under different parameters. The prediction error of cell delay in the static timing analysis process is less than 3%. This method can quickly and accurately calculate the delay of standard cells in real-time and can be applied to fast timing analysis under multiple scenarios before sign-off. [ABSTRACT FROM AUTHOR]

In order to maintain the local geometric structure of features while learning the deep nonlinear relationship between features, this paper proposes a single-layer autoencoder as a joint framework for feature selection and manifold learning. Firstly, the reconstruction capability of single-layer autoencoder is used to eliminate the single feature with weak contribution to the reconstructed sample, learn the deep nonlinear relationship of the feature, and carry out sparse regularization on the feature weight matrix. Secondly, an optimal feature subset is obtained by improving the local linear embedding algorithm to preserve the local structure among features. Finally, a new target loss function is designed and the L-BFGS algorithm is used for iterative optimization. Compared with other six unsupervised feature selection algorithms on six data sets, the experimental results show that this algorithm is superior to other unsupervised feature selection algorithms in clustering performance and classification performance. [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]

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]

In response to the overall trend and practical application of multi-threshold points in electromechanical equipment fault data detection, this paper proposes a cloud-edge collaborative electromechanical equipment fault detection method based on a variational autoencoder with gated recurrent unit (VAE-GRU). A cloud-edge collaborative electromechanical equipment fault detection architecture is structed, including a terminal equipment layer, an edge node layer, and a cloud center layer, in which electromechanical equipment is detected for faults through collaboration between the cloud center and edge nodes. The VAE-GRU model is design, where the input data is sampled by VAE, and GRU is used to capture the long-term correlation of the timing data. A dynamic threshold selection algorithm is used to calculate the fault detection threshold, that can automatically select the optimal threshold for different data sets to improve fault detection accuracy. Experimental results show that the proposed method improves the accuracy of electromechanical equipment fault detection while reducing latency, enduring the normal and stable operation of electromechanical equipment. [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]

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]