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Empowering smart city situational awareness via big mobile data

Frontiers of Information Technology & Electronic engineering 2024年第2期25卷 286-307页

作者： Zhiguang SHAN Lei SHI Bo LI Yanqiang ZHANG Xiatian ZHANG Wei CHEN State Information Center Beijing100045China School of Computer Science and Engineering Beihang UniversityBeijing100191China Zhongguancun Laboratory Beijing100094China Beijing Tendcloud Tianxia Technology Co. Ltd.Beijing100027China The State Key Lab of CAD&CG Zhejiang UniversityHangzhou310027China

Smart city situational awareness has recently emerged as a hot topic in research societies,industries,and governments because of its potential to integrate cutting-edge information technology and solve urgent challenges that modern cities *** example,in the latest five-year plan,the Chinese government has highlighted the demand to empower smart city management with new technologies such as big data and Internet of Things,for which situational awareness is normally the crucial first *** traditional static surveillance data on cities have been available for decades,this review reports a type of relatively new yet highly important urban data source,i.e.,the big mobile data collected by devices with various levels of mobility representing the movement and distribution of public and private agents in the *** especially focus on smart city situational awareness enabled by synthesizing the localization of hundreds of thousands of mobile software Apps using the Global Positioning System(GPS).This technique enjoys advantages such as a large penetration rate(∼50%urban population covered),uniform spatiotemporal coverage,and high localization *** first discuss the pragmatic requirements for smart city situational awareness and the challenges *** we introduce two suites of empowering technologies that help fulfill the requirements of(1)cybersecurity insurance for smart cities and(2)spatiotemporal modeling and visualization for situational awareness,both via big mobile *** main contributions of this review lie in the description of a comprehensive technological framework for smart city situational awareness and the demonstration of its feasibility via real-world applications.

关键词： Smart city Mobile data Situational awareness

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ELMap: Area-Driven LUT Mapping with $k$-LUT Network Exact Synthesis

ELMap: Area-Driven LUT Mapping with $k$-LUT Network Exact Sy...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Hongyang Pan Keren Zhu Fan Yang Zhufei Chu Xuan Zeng State Key Lab of Integrated Circuits and Systems School of Microelectronics Fudan University Shanghai China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Mapping to $k$ -input lookup tables ( $k$ -LUTs) is a critical process in field-programmable gate array (FPGA) synthesis. However, the structure of the subject graph can introduce structural bias, which refers to the dependency of mapping results on the inherent graph structure, often leading to suboptimal results. To address this, we present ELMap, an area-driven LUT mapping framework. It incorporates structural choice during the collapsing phase. This enables dynamic decomposition, maximizing local-to-global optimization transfer. To ensure seamless integration between the optimization and mapping processes, ELMap leverages exact $k$ -LUT synthesis to generate area-optimal sub-LUT networks. Experiments on the EPFL benchmark suite demonstrate that ELMap significantly outperforms state-of-the-art methods. Specifically, in 6-LUT mapping, ELMap reduces the average LUT area by 8.5% and improves the area-depth-product (ADP) by 5.8%. In 4-LUT remapping, it reduces the average LUT area by 17.6% and improves the ADP by 2.4%.

关键词： Europe Benchmark testing Table lookup Logic Optimization Field programmable gate arrays

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Cross-dataset Image Matching Network for Heterogeneous Palmprint Recognition 16th

Cross-dataset Image Matching Network for Heterogeneous Palm...

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16th Chinese Conference on Biometric Recognition, CCBR 2022

作者： Zou, Yuchen Zhong, Dexing Shao, Huikai School of Automation Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China Pazhou Lab Guangzhou510335 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210093 China

ISBN: (纸本)9783031202322

Palmprint recognition is one of the promising biometric technologies. Many palmprint recognition methods have excellent performance in recognition within a single dataset. However, heterogeneous palmprint recognition, i.e., mutual recognition between different datasets, has rarely been studied, which is also an important issue. In this paper, a cross-dataset image matching network (CDMNet) is proposed for heterogeneous palmprint recognition. Feature representations specific to a certain domain are learned in the shallow layer of the network, and feature styles are continuously aligned to narrow the gap between domains. Invariant feature representations in different domains are learned in the deeper layers of network. Further, a graph-based global reasoning module is used as a connection between the shallow and deeper networks to capture information between distant regions in palmprint images. Finally, we conduct sufficient experiments on constrained and unconstrained palmprint databases, which demonstrates the effectiveness of our method. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Palmprint recognition

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ELMap: Area-Driven LUT Mapping with k-LUT Network Exact Synthesis

ELMap: Area-Driven LUT Mapping with k-LUT Network Exact Synt...

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2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Pan, Hongyang Zhu, Keren Yang, Fan Chu, Zhufei Zeng, Xuan School of Microelectronics Fudan University State Key Lab of Integrated Circuits and Systems Shanghai China Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9783982674100

Mapping to k-input lookup tables (k-LUTs) is a critical process in field-programmable gate array (FPGA) synthesis. However, the structure of the subject graph can introduce structural bias, which refers to the dependency of mapping results on the inherent graph structure, often leading to suboptimal results. To address this, we present ELMap, an area-driven LUT mapping framework. It incorporates structural choice during the collapsing phase. This enables dynamic decomposition, maximizing local-to-global optimization transfer. To ensure seamless integration between the optimization and mapping processes, ELMap leverages exact k-LUT synthesis to generate area-optimal sub-LUT networks. Experiments on the EPFL benchmark suite demonstrate that ELMap significantly outperforms state-of-the-art methods. Specifically, in 6-LUT mapping, ELMap reduces the average LUT area by 8.5% and improves the area-depth-product (ADP) by 5.8%. In 4-LUT remapping, it reduces the average LUT area by 17.6% and improves the ADP by 2.4%. © 2025 EDAA.

关键词： Table lookup

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Grid4D: 4D decomposed hash encoding for high-fidelity dynamic Gaussian splatting 24

Grid4D: 4D decomposed hash encoding for high-fidelity dynami...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Jiawei Xu Zexin Fan Jian Yang Jij Xie PCA Lab VCIP College of Computer Science Nankai University State Key Laboratory for Novel Software Technology Nanjing University Nanjing China and School of Intelligence Science and Technology Nanjing University Suzhou China

ISBN: (纸本)9798331314385

Recently, Gaussian splatting has received more and more attention in the field of static scene rendering. Due to the low computational overhead and inherent flexibility of explicit representations, plane-based explicit methods are popular ways to predict deformations for Gaussian-based dynamic scene rendering models. However, plane-based methods rely on the inappropriate low-rank assumption and excessively decompose the space-time 4D encoding, resulting in overmuch feature overlap and unsatisfactory rendering quality. To tackle these problems, we propose Grid4D, a dynamic scene rendering model based on Gaussian splatting and employing a novel explicit encoding method for the 4D input through the hash encoding. Different from plane-based explicit representations, we decompose the 4D encoding into one spatial and three temporal 3D hash encodings without the low-rank assumption. Additionally, we design a novel attention module that generates the attention scores in a directional range to aggregate the spatial and temporal features. The directional attention enables Grid4D to more accurately fit the diverse deformations across distinct scene components based on the spatial encoded features. Moreover, to mitigate the inherent lack of smoothness in explicit representation methods, we introduce a smooth regularization term that keeps our model from the chaos of deformation prediction. Our experiments demonstrate that Grid4D significantly outperforms the state-of-the-art models in visual quality and rendering speed. Project page: https://***/Grid4D-web/.

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Parallel-Friendly and Work-Efficient Single Source Shortest Path Algorithm on Single-Node System 24

Parallel-Friendly and Work-Efficient Single Source Shortest ...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zhang, Jie Zhang, Yuan Sun, Yiming Cao, Huawei An, Xuejun Ye, Xiaochun Institute of Computing Technology Cas State Key Lab of Processors Beijing China School of Computer and Control Engineering Ucas Beijing China Nanjing Institute of Information Superbahn Cas Nanjing China

ISBN: (纸本)9798350319934

The Single-Source Shortest Path (SSSP) algorithm is one of the most important kernels used by a variety of high-level graph processing applications. Although having been extensively studied for single-node scenarios, SSSP algorithm still faces difficulties when being deployed on scale-free graph, which brings about redundant edge traversals, expensive atomic operations and random memory access. In this paper, three parallel-friendly and work-efficient opti-mizations are introduced to improve the performance of parallel SSSP algorithm with shared memory on the single-node system. First, a label-aware pruning technique is proposed to reduce the redundant edge traversals. Then, atomic operations caused by race conditions are eliminated by using destination-driven sub graph partitioning. Finally, we propose a double-ordered bucket strategy to further improve the efficiency of SSSP. The experiment demonstrates that the average speedup reaches to 27.58x compared with original implement for R-MAT graphs. Meanwhile, our method achieves up to 2.12x for R-MAT graphs and 4.45 x for real-world graphs on average compared to previous state-of-the-art parallel implementation. © 2022 IEEE.

关键词： Memory architecture

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Source Code Summarization in the Era of Large Language Models

arXiv

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arXiv 2024年

作者： Sun, Weisong Miao, Yun Li, Yuekang Zhang, Hongyu Fang, Chunrong Liu, Yi Deng, Gelei Liu, Yang Chen, Zhenyu State Key Laboratory for Novel Software Technology Nanjing University Nanjing China College of Computing and Data Science Nanyang Technological University Singapore Singapore School of Computer Science and Engineering University of New South Wales Sidney Australia School of Big Data and Software Engineering Chongqing University Chongqing China

To support software developers in understanding and maintaining programs, various automatic (source) code summarization techniques have been proposed to generate a concise natural language summary (i.e., comment) for a given code snippet. Recently, the emergence of large language models (LLMs) has led to a great boost in the performance of code-related tasks. In this paper, we undertake a systematic and comprehensive study on code summarization in the era of LLMs, which covers multiple aspects involved in the workflow of LLM-based code summarization. Specifically, we begin by examining prevalent automated evaluation methods for assessing the quality of summaries generated by LLMs and find that the results of the GPT-4 evaluation method are most closely aligned with human evaluation. Then, we explore the effectiveness of five prompting techniques (zero-shot, few-shot, chain-of-thought, critique, and expert) in adapting LLMs to code summarization tasks. Contrary to expectations, advanced prompting techniques may not outperform simple zero-shot prompting. Next, we investigate the impact of LLMs’ model settings (including top_p and temperature parameters) on the quality of generated summaries. We find the impact of the two parameters on summary quality varies by the base LLM and programming language, but their impacts are similar. Moreover, we canvass LLMs’ abilities to summarize code snippets in distinct types of programming languages. The results reveal that LLMs perform suboptimally when summarizing code written in logic programming languages compared to other language types (e.g., procedural and object-oriented programming languages). Finally, we unexpectedly find that CodeLlama-Instruct with 7B parameters can outperform advanced GPT-4 in generating summaries describing code implementation details and asserting code properties. We hope that our findings can provide a comprehensive understanding of code summarization in the era of *** Codes 68-04 Copyright © 202

关键词： Quality control

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Affinity Propagation Hierarchical Memetic Algorithm for Multimodal Multi-Objective Flexible Job Shop Scheduling With Variable Speed

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IEEE Transactions on Evolutionary Computation 2024年

作者： Luo, Cong Li, Xinyu Gong, Wenyin Gao, Liang Huazhong University of Science and Technology State Key Lab of Digital Manufacturing Equipment and Technology School of Mechanical Science and Engineering Wuhan430074 China China University of Geosciences School of Computer Science Wuhan430074 China

The flexible job shop scheduling, as the most typical production mode in industrial manufacturing, aims to improve production efficiency. However, the proposal of energy-saving and emission-reduction policy implies that it is impossible to increase the processing speed to improve productivity, and energy consumption is also becoming another important optimization objective. For the multi-objective flexible job shop scheduling problem, the optimization process tends to converge faster in some regions. This is because different scheduling sequences obtain the same objective values, i.e. there is a multimodal characteristic, which is still hardly investigated. Therefore, optimizing the decision space and the objective space simultaneously has become an urgent challenge that needs to be solved. To overcome the above challenges, we model the multimodal multi-objective flexible job shop scheduling problem with variable speed (MMFJSP-S) and propose an affinity propagation hierarchical memetic algorithm (APHMA) to minimize makespan and total energy consumption. Firstly, four problem-specific neighborhood structures are employed to enhance the convergence;Then, an affinity propagation clustering combined with the random forests strategy is proposed to classify the global and local Pareto sets;Finally, a hierarchical environmental selection strategy is designed to ensure the convergence and diversity in the decision and objective spaces. Evaluations against seven advanced algorithms on MK and DP benchmarks demonstrate the competitive performance of APHMA in solving MMFJSP-S. © 1997-2012 IEEE.

关键词： Scheduling algorithms

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Estimation of the User Contribution Rate by Leveraging Time Sequence in Pairwise Matching function-point between Users Feedback and App Updating Log

arXiv

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arXiv 2023年

作者： Duan, Shiqi Liu, Jianxun Xiao, Yong Zhang, Xiangping School of Computer Science and Engineering Hunan University of Science and Technology Hunan China Hunan Key Lab for Services Computing and Novel Software Technology Hunan China

Mobile applications have become an inseparable part of people’s daily life. Nonetheless, the market competition is extremely fierce, and apps lacking recognition among most users are susceptible to market elimination. To this end, developers must swiftly and accurately apprehend the requirements of the wider user base to effectively strategize and promote their apps’ orderly and healthy evolution. The rate at which general user requirements are adopted by developers, or user contribution, is a very valuable metric that can be an important tool for app developers or software engineering researchers to measure or gain insight into the evolution of app requirements and predict the evolution of app software. Regrettably, the landscape lacks refined quantitative analysis approaches and tools for this pivotal indicator. To address this problem, this paper exploratively proposes a quantitative analysis approach based on the temporal correlation perception that exists in the app update log and user reviews, which provides a feasible solution for quantitatively obtaining the user contribution. The main idea of this scheme is to consider valid user reviews as user requirements and app update logs as developer responses, and to mine and analyze the pairwise and chronological relationships existing between the two by text computing, thus constructing a feasible approach for quantitatively calculating user contribution. To demonstrate the feasibility of the approach, this paper collects data from four Chinese apps in the App Store in mainland China and one English app in the U.S. region, including 2,178 update logs and 4,236,417 user reviews, and from the results of the experiment, it was found that 16.6%-43.2% of the feature of these apps would be related to the drive from the online popular user requirements. Copyright © 2023, The Authors. All rights reserved.

关键词： Application programs

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Snapshot boosting: a fast ensemble framework for deep neural networks

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Science China(Information Sciences) 2020年第1期63卷 77-88页

作者： Wentao ZHANG Jiawei JIANG Yingxia SHAO Bin CUI Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications Department of Computer Science Beijing Key Lab of Intelligent Telecommunications Software and Multimedia School of Computer ScienceBeijing University of Posts and Telecommunications Key Lab of High Confidence Software Technologies Department of Computer SciencePeking University

Boosting has been proven to be effective in improving the generalization of machine learning models in many fields. It is capable of getting high-diversity base learners and getting an accurate ensemble model by combining a sufficient number of weak learners. However, it is rarely used in deep learning due to the high training budget of the neural network. Another method named snapshot ensemble can significantly reduce the training budget, but it is hard to balance the tradeoff between training costs and diversity. Inspired by the ideas of snapshot ensemble and boosting, we propose a method named snapshot boosting. A series of operations are performed to get many base models with high diversity and accuracy, such as the use of the validation set, the boosting-based training framework, and the effective ensemble strategy. Last, we evaluate our method on the computer vision(CV) and the natural language processing(NLP) tasks, and the results show that snapshot boosting can get a more balanced trade-off between training expenses and ensemble accuracy than other well-known ensemble methods.

关键词： ensemble learning deep learning boosting neural network snapshot ensemble

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