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Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the popularity of the Internet, information on the web is spreading to the public at an astonishing speed. However, false information and rumors are also rapidly spreading due to the cascade effect, causing great harm to society. Finding the source of rumor spread on social networks plays a crucial role in suppressing the spread of rumors. Most of the traditional rumor source localization methods fail to integrate multi-source features and the accuracy of localization still needs to be further improved. Therefore, this paper proposes a deep learning-based rumor source localization method that identifies the rumor source based on multi-source features observed in nodes affected by rumors. This method first obtains the influence vectors of nodes based on the similarity of influence between the node and observed nodes. Then, it uses autoencoder networks to encode the influence vectors of nodes, obtaining new embedding representations that contain node information, diffusion paths, and propagation time information. Finally, it calculates the probability of nodes being the source based on their new influence vectors to locate the rumor source. Experimental results on two simulated datasets and four real datasets show that compared with other methods, the proposed method can locate the rumor source at a faster speed and improve the accuracy of rumor source localization by more than 25%. [ABSTRACT FROM AUTHOR]

At present, many research teams have conducted in-depth research on minority language machine translation for South and Southeast Asia. However, as the official language of Pakistan, Urdu has limited data resources and a significant gap from Chinese, resulting in a lack of targeted research on Chinese-Urdu machine translation methods. To address this issue, this paper proposes a Chinese-Urdu neural machine translation model based on Transformer and incorporating Urdu part-of-speech sequence prediction. Firstly, Transformer is used to predict the part-of-speech sequence of the target language Urdu. Then, the translation model's prediction results are combined with the part-of-speech sequence prediction model's results to jointly predict the final translation, thereby integrating language knowledge into the translation model. Experimental results on a small-scale Chinese-Urdu dataset show that the proposed method has a BLEU score of 0.13 higher than the baseline model on the dataset, achieving significant improvement. [ABSTRACT FROM AUTHOR]

Event argument role classification is a subtask in event extraction, which aims to assign corresponding roles to candidate arguments in the event. Event corpus labeling rules are complicated and labor-intensive, and there is a lack of relevant labeling texts in many languages. Zero-shot cross-lingual event argument role classification can use source-side corpus with rich annotations to build a model, and then apply it directly to a target-side counterpart task where the labeled corpus is scarce. Focusing on the commonalities of the dependency structure of event texts between different languages, this paper further proposes a method that uses the BiGRU network to encode dependency paths connecting trigger words to candidate arguments. The proposed encoder can be flexibly integrated into several mainstream models in a deep learning framework for event argument role classification. The experimental results demonstrate that the proposed method is more effective in completing cross-lingual migration and improving the classification performance of multiple baselines. [ABSTRACT FROM AUTHOR]

In order to minimize the processing latency and energy consumption for user tasks in Mobile Edge Computing (MEC) and enhance user experience, this paper focuses on the computation offloading problem in a multi-user, multi-MEC server scenario under constraints on computational resources. With the objective of minimizing the weighted sum of user completion time and energy consumption, the problem is tackled by first decoupling it into two sub-problems: offloading decision and computation resource allocation. The Whale Optimization Algorithm is employed to solve the offloading decision problem, enhancing convergence speed by introducing a nonlinear convergence factor and inertial weight. A feedback mechanism is introduced to prevent local optima, yielding offloading decisions with higher probability of feasibility. The resource allocation problem is addressed using the Lagrange multiplier method to obtain the optimal computation resource allocation for each offloading decision. Finally, stable converged solutions are obtained through multiple iterations. Simulation results demonstrate that, compared to other benchmark solutions, the proposed approach reduces the system overhead by up to 44.6%. [ABSTRACT FROM AUTHOR]

The large amount of medical information carried in the electronic medical record can help doctors better understand the situation of patients and assist doctors in clinical diagnosis. As the two core tasks of Chinese electronic medical record (EMR) information extraction, named entity recognition and entity relationship extraction have become the main research directions. Its main goal is to identify the medical entities in the EMR text and extract the medical relationships between the entities. This paper systematically expounds the research status of Chinese electronic medical record, points out the important role of named entity recognition and entity relationship extraction in Chinese electronic medical record information extraction, then introduces the latest research results of named entity recognition and relationship extraction algorithm for Chinese electronic medical record information extraction, and analyzes the advantages and disadvantages of each model in each stage. In addition, the current problems of Chinese EMR are discussed, and the future research trend is prospected. [ABSTRACT FROM AUTHOR]

The maneuver identification of fighter aircraft is the basis for judging their tactical intentions, but the existing maneuver identification methods have weak real-time performance and lack interpretability, which cannot meet the real-time requirements in air combat and are not conducive to humanmachine trust. This paper designs a real-time maneuver identification algorithm based on early timeseries classification, which divides the complete maneuver into maneuver units and uses ensemble learning algorithm to recognize and monitor the maneuver units in real-time, in order to achieve real-time requirements and obtain high recognition accuracy. The algorithm uses interpretable models and explains the model through feature contribution, making the model more transparent and reducing the decision risk for air combat decision-makers. Nine different maneuvers, such as hovering and jackknifing, are selected for simulation experiments, which proves that the algorithm can complete the identification with only the first 20% of the sample data of the time series observed, and the identification accuracy can reach 93%. [ABSTRACT FROM AUTHOR]

Distant supervision relation extraction uses the automatic alignment of natural language texts and knowledge bases to generate labeled training datasets, solving the problem of manual sample labeling. In the current research, most distant supervision does not pay attention to the long-tail data, so most of the sentence bags obtained by distant supervision contain too few sentences. These sentence bags cannot truly and comprehensively express the data itself. This paper proposes a distant supervised relation extraction model (PG+PTATT) based on position-type attention mechanism and graph convolutional network. According to the similarity between sentence bags, Graph Convolutional Networks (GCN) aggregate the implicit high-level features of similar sentence bags to optimize the sentence bags and obtain more prosperous and more comprehensive feature information of the sentence bags. At the same time, an attention mechanism, Position-Type Attention (PTATT) is constructed, which can solve the problem of wrong labels in distant supervision relation extraction: using the position relationships between entity words and non-entity words and type relationships are modeled to reduce the impact of noisy words. The proposed model is experimentally verified on the dataset New York Times (NYT), and the experimental results show that the proposed model can effectively solve the problems existing in distant supervision relation extraction; and it can effectively improve the accuracy of relation extraction.. [ABSTRACT FROM AUTHOR]

With the development of educational informatization, educational data presents characteristics such as high feature counts and high redundancy, resulting in the classification accuracy of current classification algorithms not being ideal on educational data. Therefore, this paper proposes a hybrid feature selection algorithm (RF-ATPSO) that integrates feature weighting algorithm with improved particle swarm optimization algorithm. The algorithm first uses the RELIEF-F algorithm to calculate the weights of each feature, removes redundant features, and then uses the improved particle swarm optimization algorithm to search for the optimal feature subset in the filtered feature set. Experimental results show that on 6 UCI public datasets, after feature selection using the RF-ATPSO algorithm, the average accuracy is improved by 10.04%, and the average feature subset size is the smallest and the convergence speed is the fastest. In the student academic performance portrait feature dataset, the algorithm achieves high classification accuracy with a smaller feature subset size, with an average accuracy of 94.77%, which is significantly better than other feature selection algorithms. The experiment fully demonstrates the practical application significance of this algorithm. [ABSTRACT FROM AUTHOR]

The expression of sentiment texts in different domains are different, so it is usually necessary to train the corresponding sentiment analysis model for each domain. In order to solve the problem that one model cannot be used for multi-domain sentiment analysis, this paper proposes a multi-domain text sentiment analysis method based on prompt tuning, called MSAPT. With the help of hard prompts, indicating the domain of the emotional text and the selected emotional labels, the model is prompted to draw on its knowledge of different domain sentiment analysis. Then, a unified "generalized model" is pretrained for sentimental analysis. In downstream learning of various domain texts, the model is frozen and prompt tuning is used to make the model learn the characteristics of emotional text in each downstream domain. MSAPT only requires saving a model and some prompts with far fewer parameters than the model for multi-domain sentiment analysis. Experiments were conducted using multiple datasets of emotional text in different fields, and the results show that MSAPT outperforms model fine-tuning when only prompted tuning is applied. Finally, the length of prompt tuning, hard prompt adapted to specific domains, soft prompt and the size of intermediate training dataset are ablated respectively, to prove their impact on the effectiveness of sentiment analysis. [ABSTRACT FROM AUTHOR]

The traditional express terminal distribution mode has problems such as redundant construction of express outlets and overlapping delivery paths, and the joint distribution model can effectively solve these problems. Therefore, this paper studies the location path of express terminal outlets in the case of simultaneous receiving and dispatching and uncertain receiving demand under the joint distribution model. Firstly, a two-stage mathematical optimization model is established to deal with the problem of uncertain receipt volume by introducing random chance constraints. Secondly, a hybrid heuristic algorithm based on genetic algorithm and adaptive large neighborhood search algorithm is designed. Finally, numerical experiments show that the designed hybrid algorithm has a faster convergence speed and better solution quality than the traditional genetic algorithm. Too high or low risk acceptance of the optimization scheme in the random demand environment will lead to the increase of cost. With the increase of the ratio of customer receiving and dispatching volume, the cost of express terminal distribution first decreases and then increases, The nearest outlet return strategy can effectively reduce the distribution cost of enterprises. [ABSTRACT FROM AUTHOR]

Clustering is the main task of machine learning, and is also the core work of granular computing, namely information granulation. At present, most of granular computing based clustering algorithms only utilize the granule features without taking the granule structure into account, especially in the information field where algebraic structure is widely used. From the perspective of granular computing, this paper proposes a clustering method based on algebraic granularity (CMAG). Firstly, the algebraic granularity is newly formulated with the granule structure of an algebraic binary operator. Secondly, the CMAG is proposed with granules of incorporating congruence partition and granule structure of homeomorphic projection. Finally, the CMAG is experimentally compared with the tolerance domain model and the quotient space model, and the results show that the CMAG has better structural completeness and practical robustness. The CMAG can enrich and extend the granular computing theory from granule structure, and will provide a theoretical basis for the combination of granular computing methods and machine learning theory. [ABSTRACT FROM AUTHOR]

The development of medical imaging technology has generated a massive amount of medical image data, which reflects the internal structural features of the human body. Medical image segmentation technology can improve the efficiency of medical diagnosis, making it an important assistive tool for modern medical diagnosis. However, noise or artifacts that are inevitably present in the imaging process bring great challenges to the segmentation work. In existing segmentation models, single-frame medical image semantic segmentation models do not consider the relationship between image frames, while video semantic segmentation models utilize temporal information but have some limitations in edge extraction. To address these issues, this paper proposes a U-Net-based temporal semantic segmentation model modified by optical flow. This model can extract optical flow information between consecutive frames and perform feature extraction and weight allocation on the current frame and optical flow for correction. Experiments show that the model obtains optimal results on three evaluation metrics, namely Dice similarity, pixel accuracy and cross-merge ratio, on different types of datasets, namely Drosophila electron micrographs, combined healthy abdominal organ segmentation and coronary angiogram, which validate the effectiveness and generalization of the proposed model. [ABSTRACT FROM AUTHOR]

Intelligent vehicular ad hoc networks (VANETs) are the core of intelligent transportation systems, in recent years, it has received increasing attentions from the academic community. However, due to the openness and fragility, VANETs are confronted with many security problems. In order to solve the problems such as two-way authentication between intelligent vehicles and nearby RSUs, exchange of session keys and anonymity of intelligent vehicles, this paper proposes an anonymous authentication and key exchange protocol in the intelligent vehicle networks. In the protocol, an identity-based digital signature algorithm is designed to enable the intelligent vehicle to send authentication information to the nearby road side unit (RSU) in a completely anonymous manner. After the RSU validates the authentication information, a message authentication code will be calculated and sent to the intelligent vehicle as the response to realize two-way authentication. In addition, during the anonymous authentication process, the session key can be negotiated for subsequent secure communication. The protocol is designed based on the identity cryptosystem, which does not need complex certificate management. The performance evaluation shows that this protocol can be effectively deployed in intelligent vehicle application scenarios with highly sensitive information. [ABSTRACT FROM AUTHOR]

Due to frequent memory access, graph neural network (GNN) often has low resource utilization when running on GPU. Existing inference frameworks, which do not consider the irregularity of GNN input, may exceed GPU memory capacity when directly applied to GNN inference tasks. For GNN inference tasks, it is necessary to pre-analyze the memory occupation of concurrent tasks based on their input characteristics to ensure successful co-location of concurrent tasks on GPU. In addition, inference tasks submitted in multi-tenant scenarios urgently need flexible scheduling strategies to meet the quality of service requirements for con-current inference tasks. To solve these problems, this paper proposes GNNSched, which efficiently manages the co-location of GNN inference tasks on GPU. Specifically, GNNSched organizes concurrent inference tasks into a queue and estimates the memory occupation of each task based on a cost function at the operator level. GNNSched implements multiple scheduling strategies to generate task groups, which are iteratively submitted to GPU for concurrent execution. Experimental results show that GNNSched can meet the quality of service requirements for concurrent GNN inference tasks and reduce the response time of inference tasks. [ABSTRACT FROM AUTHOR]

The fault detection rate is one of the main parameters of the software reliability model. Different forms of fault detection rates have different functions. This paper focuses on the impact of fault detection rate on software reliability and proposes two empirical analysis solutions based on information entropy and superi-or-inferior distance decision algorithm: one solution is single-reliabilitygrowth-model single-failure-data-set multi-fault-detection-rate plan and the other is multiple-reliabilitygrowth-model multi-failure-data-set multi-fault-detection-rate. This paper aims to comprehensively analyze the power effect of fault detection rate. According to the experimental analysis, for a single reliability model and a single data set, the impact of failure detection rate on software reliability is mainly related to the failure data set. The performance of different fault detection rates under different data sets is quite different. Under the multiple-data-sets mul-tiple-software-reliability-models situation, the comprehensive performance of the software reliability model corresponding to the power function and the S-type fault detection rate is better, and the comprehensive performance of the software reliability model corresponding to the exponential fault detection rate is poor. The research has a certain guiding role in the selection of parameters in software reliability modeling and the determination of the optimal release time. [ABSTRACT FROM AUTHOR]

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the time-varying communication delay and unknown interference problems in uncertain nonlinear second-order multi-agent systems, this paper proposes a robust adaptive flocking control law. In order to make the second-order multi-agent systems have better anti-interference ability, this paper designs a robust adaptive operator based on the statement information of agent's position and speed, and realizes the system under the time-varying communication delay disturbance. By using the Lyapunov-Krasovskii method to construct the energy function, the network connectivity of the multiagent system is proved, the speed of the agent converges to the speed of the virtual leader, and the convergence condition of the multi-agent system with time-varying communication delay is given. The simulation experiment results showthat the multi-agent system can achieve rapid convergence and form a stable topology under different interference intensities and communication delays, which proves that the proposed method is correct and effective. [ABSTRACT FROM AUTHOR]

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Scientific and effective water quality prediction is especially important for water resources management and water pollution early warning. The accuracy of the traditional prediction model is somewhat limited by the existence of nonlinearity, non-smoothness, fuzziness, seasonality and other characteristics of water quality index series. This paper combines the characteristics of autoregressive integrated moving average ARIMA model and classical fuzzy time series model, and proposes a new model of fuzzy time series water quality prediction based on dynamic affiliation. First, the use of fuzzy C-mean clustering from the original data to build the affiliation series; second, the use of the classical time series model to predict different sub-subordination series to get the dynamic affiliation; finally, defuzzification to get the predicted value of water quality indicators. The new proposed model in this paper was applied to short-term prediction of water quality indexes at a section of Minjiang River, and compared with the classical fuzzy time series model, ARIMA multiplicative seasonal model. The experimental results show that the RMSE, MAPE and MAE of the new model are better than the classical fuzzy time series model and ARIMA multiplicative seasonal model, so the new prediction model greatly improves the prediction accuracy and can provide valuable reference for water pollution prevention and control. [ABSTRACT FROM AUTHOR]

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problems of high-cost, untimely and insufficient overseas material transportation and delivery, in order to effectively plan the overseas anti-terrorism offshore supply scheme, this paper explores the efficient security mode of transportation and delivery, and establishes an overseas delivery scenario for anti-terrorism operations of bases in a certain area. By closely combining the characteristics of overseas transportation and delivery, Anylogic multi-agent is used to carry out the simulation is carried out, and two sets of overseas delivery schemes are compared. The results show that the model has good applicability for overseas material transportation and delivery, which can reduce the delivery cost, and also has certain reference significance for the exploration of overseas delivery modes. [ABSTRACT FROM AUTHOR]

This paper introduces the international mainstream cryptographic algorithms AES and SHA, and summarizes the development status of cryptographic algorithm instructions in the current mainstream general processor architecture. In order to improve the performance of general purpose processor in the field of cryptographic security, an extended instruction set of AES and SHA cryptographic algorithm for general purpose processor is designed, and an instruction execution unit of AES and SHA cryptographic algorithm that can be fully pipeline execute are realized, evaluated and optimized. The operating frequency of the instruction execution unit can reach 2.0 GHz, the total area is 17 644 μm², and the total power consumption is 59.62 mW. Compared with the software implementation with the general instructions, the minimum speedup for AES cryptographic algorithm is 8.90 times, the minimum speedup for SHA cryptographic algorithm is 4.47 times and its speedup can reach 19.30 times in the case of fully pipelined execution of instructions, which significantly improves the performance of the processor in executing AES and SHA cryptographic algorithms, It is expected to be applied to general purpose processors and further enhance the competitiveness of general purpose processor chips in the field of cryptographic security applications. In addition, the cryptographic algorithm instruction execution unit can also be encapsulated into an IP specially used to support cryptographic algorithm and applied to a special chip in the field of cryptographic security. [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]

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]

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]

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]

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 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]

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]