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IEEE International Requirements Engineering Conference Workshops (REW)

作者： Yitao Bu Hongbin Xiao Zhi Li Xiaolan Xie Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin China College of Information Science and Engineering Guilin University of Technology Guilin China Key Laboratory of Embedded Technology and Intelligent Information Processing Guangxi Universities Guilin University of Technology Guilin China

ISBN: (数字)9798350395518

ISBN: (纸本)9798350395525

As a new technology for deploying applications and services in the cloud, microservice architecture is becoming a hot topic in IT architecture today. It overcomes the problems of low development efficiency and difficult maintenance of traditional monolithic architecture, and attracts more and more enterprises to explore and practice. However, also many enterprises refuse to use microservices because improper decomposition of microservices results in a mismatch between system requirements and services, making the system expensive to build and expensive to run. Therefore, this paper proposes a microservice decomposition approach driven by sub-requirement references in Jackson's problem diagram (SRME), using requirements to drive the design of software architecture. By dividing the functional requirements, we build a problem diagram that can show more business details, obtain the reference relationship set between sub-requirements and design domains, and obtain the decomposition results of microservices according to the set partition steps. Finally, according to the service granularity and the security of functional requirements, the decomposition results are adjusted to obtain microservice candidates. A working example of a cargo tracking system demonstrates the applicability and feasibility of this method. The microservice decomposition results obtained through this method match services with requirements, and due to its consideration of performance and associated databases, they have better afferent coupling.

关键词： Couplings Waste materials Software architecture Databases Conferences Microservice architectures Computer architecture

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NuScenes-QA: A Multi-Modal Visual Question Answering Benchmark for Autonomous Driving Scenario

arXiv

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arXiv 2023年

作者： Qian, Tianwen Chen, Jingjing Zhuo, Linhai Jiao, Yang Jiang, Yu-Gang Academy for Engineering and Technology Fudan University China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China

We introduce a novel visual question answering (VQA) task in the context of autonomous driving, aiming to answer natural language questions based on street-view clues. Compared to traditional VQA tasks, VQA in autonomous driving scenario presents more challenges. Firstly, the raw visual data are multi-modal, including images and point clouds captured by camera and LiDAR, respectively. Secondly, the data are multi-frame due to the continuous, real-time acquisition. Thirdly, the outdoor scenes exhibit both moving foreground and static background. Existing VQA benchmarks fail to adequately address these complexities. To bridge this gap, we propose NuScenes-QA, the first benchmark for VQA in the autonomous driving scenario, encompassing 34K visual scenes and 460K question-answer pairs. Specifically, we leverage existing 3D detection annotations to generate scene graphs and design question templates manually. Subsequently, the question-answer pairs are generated program-matically based on these templates. Comprehensive statistics prove that our NuScenes-QA is a balanced large-scale benchmark with diverse question formats. Built upon it, we develop a series of baselines that employ advanced 3D detection and VQA techniques. Our extensive experiments highlight the challenges posed by this new task. Codes and dataset are available at https://***/qiantianwen/NuScenes-QA. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

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In-situ synthesized SiC/TiC/SiTiOC hybrid nanofiber mats for broad-range temperature microwave absorption

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Science China Technological Sciences 2025年第6期68卷 183-196页

作者： Yashan HUO Yuetong QIAN Minshan ZHAO Lei ZHANG Yujia TAN Zhihui HE Peng MIAO Fuchun ZHANG Wei CUI Renchao CHE School of Physics and Electronic Information Yan'an University Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Laboratory of Advanced Materials Shanghai Key Lab of Molecular Catalysis and Innovative Materials Academy for Engineering &Technology Advanced Coatings Research Center of Ministry of Education of China Fudan University School of Materials and Chemical Engineering Xi'an Technological University School of Materials Science & Engineering Tongji University College of Physics Donghua University

Efficient, temperature-insensitive electromagnetic attenuation materials are critical for high microwave absorption at high temperatures. This study presents SiC/TiC/SiTiOC hybrid nanofiber mats constructed using an in-situ synthesis method;these mats incorporate SiC/TiC as the microwave loss unit and SiTiOC as the antioxidant unit, offering an innovative approach to high-temperature microwave absorption. The introduction of high-conductivity TiC not only optimizes the dielectric constant and impedance matching of the material but also enhances its thermal cycling stability. This design ensures steady electromagnetic attenuation, enabling the hybrid nanofiber mats to maintain robust absorption performance in the X-band, even at high temperatures. At 873 K, the material demonstrates an effective absorption bandwidth of 3.6 GHz and a minimum reflection loss of-45.55 dB. Compared to single-system absorbers, such as SiC/C nanofiber materials, with a single system, the multicomponent SiC/TiC/SiTiOC hybrid nanofiber mats deliver reliable absorption performance in the temperature range of 293–873 K. This study confirms that highly conductive titanium-based ceramic materials used for dielectric property regulation effectively address the challenges of poor temperature stability and limited reusability in high-temperature carbon-ceramic absorbing materials, providing valuable insights and guidance for designing efficient microwave absorbers that can operate across a wide temperature range.

关键词： electrospinning SiC/TiC/SiTiOC hybrid nanofiber mats high-temperature microwave absorption dielectric properties thermal stability

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DeTrack: in-model latent denoising learning for visual object tracking 24

DeTrack: in-model latent denoising learning for visual objec...

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Proceedings of the 38th International Conference on Neural information processing Systems

作者： Xinyu Zhou Jinglun Li Lingyi Hong Kaixun Jiang Pinxue Guo Weifeng Ge Wenqiang Zhang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Shanghai Engineering Research Center of AI & Robotics Academy for Engineering and Technology Fudan University Shanghai China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China and Shanghai Engineering Research Center of AI & Robotics Academy for Engineering and Technology Fudan University Shanghai China

ISBN: (纸本)9798331314385

Previous visual object tracking methods employ image-feature regression models or coordinate autoregression models for bounding box prediction. Image-feature regression methods heavily depend on matching results and do not utilize positional prior, while the autoregressive approach can only be trained using bounding boxes available in the training set, potentially resulting in suboptimal performance during testing with unseen data. Inspired by the diffusion model, denoising learning enhances the model's robustness to unseen data. Therefore, We introduce noise to bounding boxes, generating noisy boxes for training, thus enhancing model robustness on testing data. We propose a new paradigm to formulate the visual object tracking problem as a denoising learning process. However, tracking algorithms are usually asked to run in real-time, directly applying the diffusion model to object tracking would severely impair tracking speed. Therefore, we decompose the denoising learning process into every denoising block within a model, not by running the model multiple times, and thus we summarize the proposed paradigm as an in-model latent denoising learning process. Specifically, we propose a denoising Vision Transformer (ViT), which is composed of multiple denoising blocks. In the denoising block, template and search embeddings are projected into every denoising block as conditions. A denoising block is responsible for removing the noise in a predicted bounding box, and multiple stacked denoising blocks cooperate to accomplish the whole denoising process. Subsequently, we utilize image features and trajectory information to refine the denoised bounding box. Besides, we also utilize trajectory memory and visual memory to improve tracking stability. Experimental results validate the effectiveness of our approach, achieving competitive performance on several challenging datasets. The proposed in-model latent denoising tracker achieve real-time speed, rendering denoising learning

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Characterizing and Understanding Development of Social Computing Through DBLP: A Data-Driven Analysis

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Journal of Social Computing 2022年第4期3卷 287-302页

作者： Jiaqi Wu Bodian Ye Qingyuan Gong Atte Oksanen Cong Li Jingjing Qu Felicia F.Tian Xiang Li Yang Chen the Shanghai Key Lab of Intelligent Information Processing the School of Computer ScienceFudan UniversityShanghai 200438China the Faculty of Social Sciences Tampere UniversityTampere 33100Finland School of Information Science and Technology Fudan UniversityShanghai 200438China Shanghai Artificial Intelligence Laboratory Shanghai 200232China. School of Social Development and Public Policy Fudan UniversityShanghai 200433China. Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous SystemsTongji UniversityShanghai 200092China.

During the past decades,the term“social computing”has become a promising interdisciplinary area in the intersection of computer science and social *** this work,we conduct a data-driven study to understand the development of social computing using the data collected from Digital Bibliography and Library Project(DBLP),a representative computer science bibliography *** have observed a series of trends in the development of social computing,including the evolution of the number of publications,popular keywords,top venues,international collaborations,and research *** findings will be helpful for researchers and practitioners working in relevant fields.

关键词： social computing Digital Bibliography and Library Project(DBLP) bibliometric evolution visualization

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Weakly-supervised Temporal Action Localization with Adaptive Clustering and Refining network

Weakly-supervised Temporal Action Localization with Adaptive...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Hao Ren Wu Ran Xingson Liu Haoran Ren Hong Lu Rui Zhang Cheng Jin Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Peng Cheng Laboratory Shenzhen China

Weakly-supervised temporal action localization task aims to localize temporal boundaries of action instances by using only video-level labels. Existing methods primarily adopt Multi-Instance-Learning (MIL) scheme to handle this task. The effectiveness of MIL scheme depends heavily on the selection of top-k action snippets, which is unstable and requires manual tuning. To address these deficiencies, we propose an Adaptive Clustering and Refining network (ACRNet). Specifically, we present an action-aware clustering strategy that is adaptable and requires no manual tuning to separate action and background snippets of diverse videos based on intra-class activation distribution. And a cluster refining step is included to eliminate false action snippets by considering inter-class activation distribution, which greatly improves robustness and localization accuracy. Extensive experiments on THUMOS14, ActivityNet 1.2&1.3 benchmarks show that our method achieves state-of-the-art performance.

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Expectation-Constrained Programming Model for Tourist Route of Attractions Based on Uncertainty Theory 5

Expectation-Constrained Programming Model for Tourist Route ...

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5th International Conference on Advanced Electronic Materials, Computers and Software Engineering, AEMCSE 2022

作者： Huang, Zheng Ning, Yufu Liu, Fengming Shandong Normal University Business School Ji'nan China Key Lab. of Intelligent Information Processing Technology and Security in Universities of Shandong Jinan250103 China Shandong Youth University of Political Science School of Information Engineering Ji'nan China

ISBN: (数字)9781665484749

ISBN: (纸本)9781665484749

Based on the current background of scenic spot management and tourism planning in China, this paper establishes an uncertain tourism route expectation-constrainted programming model under the framework of uncertainty theory. Finally, eight attractions in Jinan are used as practical cases to illustrate the usefulness of the model. The research results show that the proposed model can effectively alleviate the problems of unreasonable allocation of tourists' time and cost in the traditional model, and make the layout and programming of routes more reasonable and efficient. © 2022 IEEE.

关键词： Computer programming

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TrafficGPT: Breaking the Token Barrier for Efficient Long Traffic Analysis and Generation

arXiv

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arXiv 2024年

作者： Qu, Jian Ma, Xiaobo Li, Jianfeng MOE Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an China

Over the years, network traffic analysis and generation have advanced significantly. From traditional statistical methods, the field has progressed to sophisticated deep learning techniques. This progress has improved the ability to detect complex patterns and security threats, as well as to test and optimize network performance. However, obstacles persist, such as the dependence on labeled data for analysis and the difficulty of generating traffic samples that follow realistic patterns. Pretrained deep neural networks have emerged as powerful tools to resolve these issues, offering improved performance by learning robust data representations from large unlabeled datasets. Despite their benefits, existing pre-trained models face challenges like token length limitation, which restricts their usefulness in comprehensive traffic analysis and realistic traffic generation. To address these challenges, we introduce TrafficGPT, a deep learning model that can tackle complex challenges related to long flow classification and generation tasks. This model uses generative pre-training with the linear attention mechanism, which allows for a substantially increased capacity of up to 12,032 tokens from the previous limit of only 512 tokens. TrafficGPT demonstrates superior performance in classification tasks, reaching state-of-the-art levels. In generation tasks, it closely resembles real traffic flows, with low JS divergence and an F1 score close to 0.5 (representing a random guess) in discriminating generated data. These advancements hold promise for future applications in both traffic flow classification and generation tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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A Block Coordinate Descent Approach to Design Constant Modulus Waveform for MIMO Radar with Low-Resolution DACs

A Block Coordinate Descent Approach to Design Constant Modul...

引用

IEEE International Conference on information Communication and Signal processing (ICICSP)

作者： Jiaying Wen Bin Liao Guangdong Key Lab of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Shenzhen China

In recent years, the problem of waveform design for multiple-input multiple-output (MIMO) radar has drawn extensive attention. Noted that the most of existing methods are devoted to systems with infinite-resolution digital-to-analog converters (DACs), which increase the hardware-cost and circuit power consumption. In this paper, the problem of constant modulus (CM) waveform design for MIMO radar which deploys low-resolution phase-only DACs is investigated. To achieve better radiation performance, the integrated sidelobe to mainlobe ratio (ISMR) is considered as the optimization metric. However, the optimization problem is difficult to solve due to the quadratic fractional function and discrete constraints. Towards this end, we employ the Dinkelbach algorithm to transform the quadratic fractional problem into a parametric subtractive-form problem. Leveraging on the block coordinate descent (BCD) method, the original multi-dimensional problem is decomposed into multiple smaller dimensional problems. We sequentially update each variable block while fixing the remaining blocks to solve each subproblem. Finally, we analyze the performance of the proposed algorithm in terms of convergence and beampattern of the wave-form, in comparison with other algorithms through numerical simulations. The results show that the proposed method can effectively achieve a low ISMR.

关键词： Measurement Power demand MIMO radar Signal processing algorithms Transforms Signal processing Numerical simulation

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Improving Event-Level Financial Sentiment Analysis with Retrieval-Augmented Multipath Chain-of-Thought Prompting 12th

Improving Event-Level Financial Sentiment Analysis with Ret...

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12th CCF Conference on BigData, BigData 2024

作者： Zhang, Yiming Ao, Xiang Yu, Guoxin He, Qing Henan Institute of Advanced Technology Zhengzhou University Zhengzhou450002 China Beijing100190 China Key Lab of Intelligent Information Processing Institute of Computing Technology CAS Beijing100190 China

ISBN: (纸本)9789819610235

Event-level Financial Sentiment Analysis (EFSA) aims to extract all the quintuples containing five sentiment elements from a given financial news text, which has gained prominence as an emerging domain recently. The present study utilizes a 4-hop Chain-of-Thought (CoT) prompting based on LLMs to predict sentiment elements in a fixed order, which neglects the interdependencies among the sentiment elements within a quintuple. Inspired by recent multi-view prompting (MvP) and CoT ideas, we propose a novel framework termed Retrieval-Augmented Multipath Chain-of-Thought (RMP-CoT) that aggregates quintuples generated by LLMs through different reasoning paths, leveraging a retrieval-augmented mechanism. Specifically, RMP-CoT integrates different element orders into CoT prompting to guide LLMs in generating multiple sentiment quintuples through the utilization of retrieval-augmented mechanism, and then selects the most plausible quintuples by voting. To investigate the effectiveness of our framework, we conduct extensive experiments on four benchmark tasks of EFSA. RMP-CoT pushes the state-of-the-art by over 6% F1 on the EFSA task and also performs quite effectively on the other sub-tasks of EFSA. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Chain-of-Thought Event-level Sentiment Analysis Financial Sentiment Analysis

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