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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Xu, Pucheng Yu, Yuecheng Zou, Rundong Shi, Jinlong Li, Yongzheng School of Computer Science Jiangsu University of Science and Technology Zhenjiang China School of Naval Architecture and Ocean Engineering Jiangsu University of Science and Technology Zhenjiang China

ISBN: (纸本)9798350350890

YOLOv7-tiny, as a lightweight variant of YOLOv7, boasts advantages of fast runtime and fewer parameters. However, when directly applied to infrared object detection, YOLOv7-tiny still faces challenges such as weak extraction of fine details, loss of semantic information, and high resource consumption. To address these issues, we propose a comprehensive balance between detection speed and accuracy in the Rapider-YOLOv7 object detection network. Firstly, it introduces the Exponential Spatial Pyramid Pooling with Channel Spatial Pyramid (Soft-SPPCSP) module to enhance the capability of the original model in extracting low contrast information from infrared images. Secondly, it designs the FO-CA attention module to strengthen the model's feature extraction capability for infrared images with weak textures. Furthermore, it devises the lightweight SFaster-net network, which incorporates SPDconv convolution for feature extraction to accelerate the model's feature extraction speed and enhance its capability to extract features from low-resolution images. Experimental results on the FLIR-v2 infrared dataset demonstrate that the mAP of the Rapider-YOLOv7 model reaches 62.6%, which is 2.9% higher than that of the original YOLOv7-tiny. Moreover, the FPS value on the RTX4060ti reaches 93. © 2024 IEEE.

关键词： Infrared imaging

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NEURAL PROGRAM SYNTHESIS WITH QUERY 10

NEURAL PROGRAM SYNTHESIS WITH QUERY

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10th International Conference on Learning Representations, ICLR 2022

作者： Huang, Di Zhang, Rui Hu, Xing Zhang, Xishan Jin, Pengwei Li, Nan Du, Zidong Guo, Qi Chen, Yunji SKL of Computer Architecture Institute of Computing Technology CAS China University of Chinese Academy of Sciences China University of Science and Technology of China China Cambricon Technologies

Aiming to find a program satisfying the user intent given input-output examples, program synthesis has attracted increasing interest in the area of machine learning. Despite the promising performance of existing methods, most of their success comes from the privileged information of well-designed input-output examples. However, providing such input-output examples is unrealistic because it requires the users to have the ability to describe the underlying program with a few input-output examples under the training distribution. In this work, we propose a query-based framework that trains a query neural network to generate informative input-output examples automatically and interactively from a large query space. The quality of the query depends on the amount of the mutual information between the query and the corresponding program, which can guide the optimization of the query framework. To estimate the mutual information more accurately, we introduce the functional space (F-space) which models the relevance between the input-output examples and the programs in a differentiable way. We evaluate the effectiveness and generalization of the proposed query-based framework on the Karel task and the list processing task. Experimental results show that the query-based framework can generate informative input-output examples which achieve and even outperform well-designed input-output examples. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

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Multimodal Large Models are Effective Action Anticipators

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IEEE Transactions on Multimedia 2025年 27卷 2949-2960页

作者： Wang, Binglu Tian, Yao Wang, Shunzhou Yang, Le Xi'an University of Architecture and Technology College of Information and Control Engineering Xi'an710055 China Northwestern Polytechnical University School of Astronautics Xi'an710072 China Peking University School of Electronic and Computer Engineering Shenzhen Graduate School Shenzhen518055 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230022 China University of Science and Technology of China School of Information Science and Technology Hefei230022 China

The task of long-term action anticipation demands solutions that can effectively model temporal dynamics over extended periods while deeply understanding the inherent semantics of actions. Traditional approaches, which primarily rely on recurrent units or Transformer layers to capture long-term dependencies, often fall short in addressing these challenges. Large Language Models (LLMs), with their robust sequential modeling capabilities and extensive commonsense knowledge, present new opportunities for long-term action anticipation. In this work, we introduce the ActionLLM framework, a novel approach that treats video sequences as successive tokens, leveraging LLMs to anticipate future actions. Our baseline model simplifies the LLM architecture by setting future tokens, incorporating an action tuning module, and reducing the textual decoder layer to a linear layer, enabling straightforward action prediction without the need for complex instructions or redundant descriptions. To further harness the commonsense reasoning of LLMs, we predict action categories for observed frames and use sequential textual clues to guide semantic understanding. In addition, we introduce a Cross-Modality Interaction Block, designed to explore the specificity within each modality and capture interactions between vision and textual modalities, thereby enhancing multimodal tuning. Extensive experiments on benchmark datasets demonstrate the superiority of the proposed ActionLLM framework, encouraging a promising direction to explore LLMs in the context of action anticipation. © 1999-2012 IEEE.

关键词： Semantics

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Study on the Impact of Urban Road Network on Commercial Layout – a Case Study of Chengdu 1

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2nd International Conference on Advanced Civil Engineering and Smart Structures, ACESS 2023

作者： Che, Lu Guo, Sidai Deng, Yuan Chen, Junliang School of Economics and Management Southwest University of Science and Technology Mianyang China School of Civil Engineering and Architecture Southwest University of Science and Technology Mianyang China School of Computer Science and Technology Southwest University of Science and Technology Mianyang China

ISBN: (数字)9789819715145

ISBN: (纸本)9789819715138

Urban road network form and commercial layout play an important role in urban development, the study of the relationship between the two can provide support for urban commercial functional area layout, road planning and commercial planning of government departments. Taking the inner city of the Third Ring Road of Chengdu as an example, this study quantitatively analyzed the correlation between road network form and retail layout by using spatial syntax and GIS spatial analysis. The results show that: (1) the distribution of different retail businesses extends from east to west, which is consistent with the distribution direction of road network. (2) The road network with high global integration degree is mainly concentrated in one ring, which has high accessibility, centrality and strong crowd gathering ability. At the same time, the integration degree of the four axes of northwest, northeast, southwest and south of the First ring center is also high, which is basically consistent with the axial development of Chengdu in recent years. (3) Road connection value and control value have a high impact on commercial outlets, especially for convenience stores and supermarkets. At the same time, the impact of road depth on retail layout is relatively low. Overall, in terms of road planning, emphasis should be placed on the coordination of urban road connection values and control values, so as to alleviate traffic pressure while promoting the further development of retail commerce. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Roads and streets

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Joint Prediction of SOH and RUL for Lithium Batteries Considering Capacity Self Recovery and Model Drift

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IEEE Internet of Things Journal 2025年第12期12卷 22187-22196页

作者： Jia, Zhen Li, Zhifei Wang, Zhengdong Liu, Zhenbao Vong, Chi-Man Xi'an University of Architecture and Technology School of Mechanical and Electrical Engineering Xi'an710055 China Northwestern Polytechnical University School of Civil Aviation Xi'an710072 China University of Macau Department of Computer and Informa tion Science Macau999078 China

Accurate monitoring of the state of health (SOH) and remaining useful life (RUL) of lithium batteries is a key technology to realize their stable and economic operation. The current data-driven methods have problems such as local prediction errors caused by capacity self-recovery effect, model drift phenomenon, and inaccurate trend prediction caused by complex degradation process. In this paper, we propose a joint SOH and RUL forecasting based on the fusion of Kolmogorov-Arnold Network (KAN), Deep Bidirectional Long Short-Term Memory Network (DBLSTM) and adaptive mechanism method (KAN-HDBLSTM-AM). The method innovatively introduces an adaptive mechanism that considers the capacity degradation trend for different prediction steps, suppresses the accumulation of errors in the model prediction process, and further improves the prediction accuracy by combining the nonlinear approximation capability of KAN and the mastery of data temporal characteristics of DBLSTM. Experimental validation shows that the proposed method can effectively overcome the prediction errors caused by capacity self-recovery and model drift, and can retain sufficient accuracy under long prediction steps, with the prediction trend being more in line with the actual change trend and the prediction error being smaller compared with other methods. © 2014 IEEE.

关键词： Prediction models

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A Secure and Efficient Information Authentication Scheme for E-Healthcare System

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computers, Materials & Continua 2023年第9期76卷 3877-3896页

作者： Naveed Khan Jianbiao Zhang Ghulam Ali Mallah Shehzad Ashraf Chaudhry Faculty of Information Technology Beijing University of TechnologyBeijing100124China Department of Computer Science Shah Abdul Latif UniversityKhairpur66111Pakistan Department of Computer Engineering Faculty of Engineering ArchitectureNisantasi UniversityIstanbul34398Turkey

The mobile cellular network provides internet connectivity for heterogeneous Internet of Things(IoT)*** cellular network consists of several towers installed at appropriate locations within a smart *** cellular towers can be utilized for various tasks,such as e-healthcare systems,smart city surveillance,traffic monitoring,infrastructure surveillance,or sidewalk *** is a primary concern in data broadcasting,particularly authentication,because the strength of a cellular network’s signal is much higher frequency than the associated one,and their frequencies can sometimes be aligned,posing a significant *** a result,that requires attention,and without information authentication,such a barrier cannot be ***,we design a secure and efficient information authentication scheme for IoT-enabled devices tomitigate the flaws in the e-healthcare *** proposed protocol security shall check formally using the Real-or-Random(ROR)model,simulated using ProVerif2.03,and informally using pragmatic *** comparison,the performance phenomenon shall tackle by the already result available in the MIRACL cryptographic lab.

关键词： IoT-enable device e-healthcare authentication edge computing

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Research on the Characteristics of Wordle Mini-Game Based on GA-BP Neural Network and K-Means Clustering Algorithm

Research on the Characteristics of Wordle Mini-Game Based on...

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2023 IEEE International Conference on Image Processing and computer Applications, ICIPCA 2023

作者： Ju, Fan Ma, Yongwei Yao, Shengjie College of Computer Science and Technology Shandong University of Architecture Shandong Jinan China

ISBN: (纸本)9798350314670

First, an ARIMA-based prediction model for the number of reported outcomes at future dates was developed. After processing the collected data, the smoothness of the number of reported outcomes was verified, and an intrinsic trend analysis of the data was performed to demonstrate the validity of the ARIMA model for predicting the number of reported outcomes at future dates, and the parameters of ARIMA were fitted using historical data to determine the final prediction model as ARIMA. secondly, one prediction model based on the GA-BP neural network algorithm was developed for the relevant percentages of future dates. At the same time, the GA algorithm was introduced for optimization to obtain the optimal initialization of the BP neural network. We use the frequency of letter occurrences together with the date as the input word feature and the correlation percentage as the output, and the prediction results are within the valid interval. Third, the word difficulty classification model and word scoring model were built based on the improved k-means clustering algorithm and PCA, respectively. To improve the drawback that the k- value in k-means algorithm cannot be determined, were introduced CH value, DB value. as indicators to evaluate the k- value, and combined the entropy weight method with Topsis to get the optimal number of clusters as 3 and the clustering index as the correlation percentage, and then applied the k-means algorithm to classify the words. We used the word difficulty ratio to determine the difficulty coefficient of each category, and also combined the score interval of each category obtained by PCA. Fourth, we found that the Wordle trivia game has the characteristic of viral spread in blocks, and more and more players choose the difficult mode as time goes by. © 2023 IEEE.

关键词： Forecasting

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Tetris:A Heuristic Static Memory Management Framework for Uniform Memory Multicore Neural Network Accelerators

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Journal of computer science & technology 2022年第6期37卷 1255-1270页

作者： Xiao-Bing Chen Hao Qi Shao-Hui Peng Yi-Min Zhuang Tian Zhi Yun-Ji Chen Distinguished Member,CCF State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China School of Computer Science and Technology University of Science and Technology of ChinaHeFei230026China Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology Shanghai200031China 不详

Uniform memory multicore neural network accelerators(UNNAs)furnish huge computing power to emerging neural network ***,with neural network architectures going deeper and wider,the limited memory capacity has become a constraint to deploy models on UNNA *** how to efficiently manage memory space and how to reduce workload footprints are urgently *** this paper,we propose Tetris:a heuristic static memory management framework for UNNA *** reconstructs execution flows and synchronization relationships among cores to analyze each tensor’s liveness *** the memory management problem is converted to a sequence permutation *** uses a genetic algorithm to explore the permutation space to optimize the memory management strategy and reduce memory *** evaluate several typical neural networks and the experimental results demonstrate that Tetris outperforms the state-of-the-art memory allocation methods,and achieves an average memory reduction ratio of 91.9%and 87.9%for a quad-core and a 16-core Cambricon-X platform,respectively.

关键词： multicore neural network accelerators liveness analysis static memory management memory reuse genetic algorithm

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An Intelligent Optimization Method of Reinforcing Bar Cutting for Construction Site

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computer Modeling in Engineering & sciences 2023年第1期134卷 637-655页

作者： Zhaoxi Ma Qin Zhao Tianyou Cang Zongjian Li Yiyun Zhu Xinhong Hei Department of Civil Engineering and Architecture Xi’an University of TechnologyXi’an710048China State Key Laboratory of Rail Transit Engineering Informatization(FSDI) Xi’an710043China Department of Computer Science and Engineering Xi’an University of TechnologyXi’an710048China

To meet the requirements of specifications,intelligent optimization of steel bar blanking can improve resource utilization and promote the intelligent development of sustainable *** one of the most important building materials in construction engineering,reinforcing bars(rebar)account for more than 30%of the cost in civil engineering.A significant amount of cutting waste is generated during the construction *** cutting waste increases construction costs and generates a considerable amount of CO_(2)*** study aimed to develop an optimization algorithm for steel bar blanking that can be used in the intelligent optimization of steel bar engineering to realize sustainable *** the proposed algorithm,the integer linear programming algorithm was applied to solve the *** was combined with the statistical method,a greedy strategy was introduced,and a method for determining the dynamic critical threshold was developed to ensure the accuracy of large-scale data *** proposed algorithm was verified through a case study;the results confirmed that the rebar loss rate of the proposed method was reduced by 9.124%compared with that of traditional distributed processing of steel bars,reducing CO_(2)emissions and saving construction *** the scale of a project increases,the calculation quality of the optimization algorithmfor steel bar blanking proposed also increases,while maintaining high calculation *** the results of this study are applied in practice,they can be used as a sustainable foundation for building informatization and intelligent development.

关键词： Building construction rebar work cutting stock problem optimization algorithm integer linear programming

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Image inpainting via Smooth Tucker decomposition and Low-rank Hankel constraint

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International Journal of computers and Applications 2023年第6期45卷 421-432页

作者： Cai, Jing Jiang, Jiawei Wang, Yibing Zheng, Jianwei Xu, Honghui Department of Forensic Science Zhejiang Police College Hangzhou China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Hangzhou China College of Computer Science and Technology Zhejiang University of Technology Hangzhou China

Image inpainting, aiming at exactly recovering missing pixels from partially observed entries, is typically an ill-posed problem. As a powerful constraint, low-rank priors have been widely applied in image inpainting to transform such problems into well-posed ones. However, the low-rank assumption of original visual data is only in an approximate mode, which in turn results in suboptimal recovery of fine-grained details, particularly when the missing rate is extremely high. Moreover, a single prior cannot faithfully capture the complex texture structure of an image. In this paper, we propose a joint usage of Smooth Tucker decomposition and Low-rank Hankel constraint (STLH) for image inpainting, which enables simultaneous capturing of the global low-rankness and local piecewise smoothness. Specifically, based on the Hankelization operation, the original image is mapped to a high-order structure for capturing more spatial and spectral information. By employing Tucker decomposition for optimizing the Hankel tensor and simultaneously applying Discrete Total Variation (DTV) to the Tucker factors, sharper edges are generated and better isotropic properties are enhanced. Moreover, to approximate the essential rank of the Tucker decomposition and avoid facing the uncertainty problem of the upper-rank limit, a reverse strategy is adopted to approximate the true rank of the Tucker decomposition. Finally, the overall image inpainting model is optimized by the well-known alternate least squares (ALS) algorithm. Extensive experiments show that the proposed method achieves state-of-the-art performance both quantitatively and qualitatively. Particularly, in the extreme case with 99% pixels missed, the results from STLH are averagely ahead of others at least 0.9dB in terms of PSNR values. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Pixels

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