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MetaCity: data-driven sustainable development of complex cities

The Innovation 2025年第2期6卷 88-103,87页

作者： Yunke Zhang Yuming Lin Guanjie Zheng Yu Liu Nicholas Sukiennik Fengli Xu Yongjun Xu Feng Lu Qi Wang Yuan Lai Li Tian Nan Li Dongping Fang Fei Wang Tao Zhou Yong Li Yu Zheng Zhiqiang Wu Huadong Guo Department of Electronic Engineering Beijing National Research Center for Information Science and Technology(BNRist)Tsinghua UniversityBeijing 100084China John Hopcroft Center for Computer Science Shanghai Jiao Tong UniversityShanghai 200240China University of Chinese Academy of Sciences Beijing 100049China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of SciencesBeijing 100101China Department of Civil and Environmental Engineering Northeastern UniversityBostonMA 02115USA School of Architecture Tsinghua UniversityBeijing 100084China School of Civil Engineering Tsinghua UniversityBeijing 100084China Big Data Research Center University of Electronic Science and Technology of ChinaChengdu 611731China JD iCity JD Technology&JD Intelligent Cities ResearchBeijing 100176China College of Architecture and Urban Planning Tongji UniversityShanghai 200292China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China These authors contributed equally

Cities are complex systems that develop under complicated interactions among their human and environmental *** generates substantial outcomes and opportunities while raising challenges including congestion,air pollution,inequality,etc.,calling for efficient and reasonable solutions to sustainable ***,booming technologies generate large-scale data of complex cities,providing a chance to propose data-driven solutions for sustainable urban *** paper provides a comprehensive overview of data-driven urban sustainability *** this review article,we conceptualize MetaCity,a general framework for optimizing resource usage and allocation problems in complex cities with data-driven *** this framework,we decompose specific urban sustainable goals,e.g.,efficiency and resilience,review practical urban problems under these goals,and explore the probability of using data-driven technologies as potential solutions to the challenge of *** the basis of extensive urban data,we integrate urban problem discovery,operation of urban systems simulation,and complex decision-making problem solving into an entire cohesive framework to achieve sustainable development goals by optimizing resource allocation problems in complex cities.

关键词： resource usage sustainable development urbanization resilience data driven urban systems simulation complex cities complexity

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TARGETED ATTACK AGAINST DEEP NEURAL NETWORKS VIA FLIPPING LIMITED WEIGHT BITS 9

TARGETED ATTACK AGAINST DEEP NEURAL NETWORKS VIA FLIPPING LI...

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9th International Conference on Learning Representations, ICLR 2021

作者： Bai, Jiawang Wu, Baoyuan Zhang, Yong Li, Yiming Li, Zhifeng Xia, Shu-Tao Tsinghua Shenzhen International Graduate School Tsinghua University China PCL Research Center of Networks and Communications Peng Cheng Laboratory China School of Data Science The Chinese University of HongKong Shenzhen China Secure Computing Lab of Big Data Shenzhen Research Institute of Big Data China Tencent AI Lab

To explore the vulnerability of deep neural networks (DNNs), many attack paradigms have been well studied, such as the poisoning-based backdoor attack in the training stage and the adversarial attack in the inference stage. In this paper, we study a novel attack paradigm, which modifies model parameters in the deployment stage for malicious purposes. Specifically, our goal is to misclassify a specific sample into a target class without any sample modification, while not significantly reduce the prediction accuracy of other samples to ensure the stealthiness. To this end, we formulate this problem as a binary integer programming (BIP), since the parameters are stored as binary bits (i.e., 0 and 1) in the memory. By utilizing the latest technique in integer programming, we equivalently reformulate this BIP problem as a continuous optimization problem, which can be effectively and efficiently solved using the alternating direction method of multipliers (ADMM) method. Consequently, the flipped critical bits can be easily determined through optimization, rather than using a heuristic strategy. Extensive experiments demonstrate the superiority of our method in attacking DNNs. The code is available at: https://***/jiawangbai/TA-LBF. © 2021 ICLR 2021 - 9th International Conference on Learning Representations. All rights reserved.

关键词： Deep neural networks

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The Integration of Blockchain and Enterprise Network: A Distributed Operation Solution 4

The Integration of Blockchain and Enterprise Network: A Dist...

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4th International Conference on Computer Information Science and Artificial Intelligence, CISAI 2021

作者： Chen, Yansheng Liu, Zhonghao Li, Jie Zhang, Weiping Guangdong Industry Polytechnic Institute of Big Data and Artificial Intelligence Applications Guangzhou China Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong

ISBN: (纸本)9781665406925

As a collaborative and associated system of industrial clusters, enterprise networks has encountered trust problems in distributed business scenarios, and the service providers or clients of its operational solutions may leave due to their own interests. Traditional trustworthiness methods are gradually losing their advantages due to the shackles (single point of failure and fairness query) or elbows (multiple iterations and performance bottlenecks) of the trust intermediary. Undoubtedly, the trustworthiness method of blockchain and the mechanism for resolving disputes are extremely important to the distributed operation mode of enterprise networks. We proposed three modes of blockchain and enterprise network integration and focused on the private chain mode of blockchain and star structure enterprise network integration, the alliance chain mode of blockchain and linear structure enterprise network integration. It also proposes applicative and distributed operation solutions based on blockchain because of its characteristic like decentralization and non-repudiation for enterprise network autonomy scenarios. Our research introduces the blockchain technology into the construction of the logistics and information flow ecosystem to master the laws of information resource flow, optimize the management of information resources, realize the innovation and development of industrial clusters, and provide theoretical reference and practical guidance. © 2021 IEEE.

关键词： Blockchain

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A Novel Adaptive Perturbation-Based Method for Long-Tailed Open Sets Recognition

SSRN

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SSRN 2023年

作者： Ngueilbaye, Alladoumbaye Zhang, Qin Yang, Hongye Zhang, Xiaolin Zhang, Peng Chen, Xiaojun Huang, Joshua Zhexue Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

Real-world data often have a long-tailed and open-ended distribution. A reliable practical machine learning system need to learn from the majority classes and also generalize to minority *** achieve this, acknowledge the minority and the novel class samples, and avoid misclassifying them into one of the majority classes are of great significance. Long-tailed Open Set Recognition aims to learn from the naturally distributed long-tailed and open-ended data, by using the easy-gained head class samples to learn the fundamental information, and also try to improve the out-of-distribution generalization ability by identifying the tail class and open-set class samples as novelty to avoid the misclassification. The key challenges for long-tailed open set recognition is the tail and open-set sensitivity, since they can be easily biased towards dominant classes and perform ineffectively on close-set dataset classes. Inspired by techniques on open set recognition, in this paper, we propose a Class-aware Input Preprocessing (CLAIP) approach to improve the detection of the Out-of-distribution samples by perturbing the input images. In this approach, we employ two techniques to leverage its performance. First, we introduce the middle class samples as auxiliary data during the network training process to force each category's boundaries. Second, we add a Class-aware Input Preprocessing module that can be easily plugged into the testing process by perturbing the salient regions of the input images to further improve the out-of-distribution generalization. The experimental results show that CLAIP achieves high quality and significant performance compared to the existing solutions. © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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OblivTime: Oblivious and Efficient Interval Skyline Query Processing Over Encrypted Time-Series data

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IEEE Transactions on Services computing 2025年第03期18卷 1602-1617页

作者： Ouyang, Huajie Zheng, Yifeng Wang, Songlei Hua, Zhongyun Harbin Institute of Technology School of Computer Science and Technology Guangdong Shenzhen518055 China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518055 China

Time-series data is prevalent in many applications like smart homes, smart grids, and healthcare. And it is now increasingly common to store and query time-series data in the cloud. Despite the benefits, data privacy concerns in such outsourced services are pressing, making it imperative to embed privacy assurance mechanisms from the outset. Most existing related works have been focused on querying for different types of aggregate statistics. In this paper, we instead focus on the secure support for advanced interval skyline queries, which allow to identify time series that are not dominated by any other time series within a query time interval. This is valuable for time-series data analytics in applications like remote health monitoring (e.g., identifying patients with high heart rates in a certain week). We present OblivTime, a new system framework for oblivious and efficient interval skyline query processing over encrypted time-series data. OblivTime is built from a synergy of time-series data analytics, lightweight cryptography, and GPU parallel computing, achieving stronger security guarantees and lower online query latency over the state-of-the-art prior work. Extensive experiments demonstrate that OblivTime can achieve up to 666\times speedup in online query latency over the state-of-the-art prior work. © 2008-2012 IEEE.

关键词： Structured Query Language

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VM-Rec: A Variational Mapping Approach for Cold-start User Recommendation

arXiv

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arXiv 2023年

作者： Zheng, Linan Chen, Jiale Liu, Pengsheng Zhang, Guangfa Fang, Jinyun University of Chinese Academy of Sciences Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing China College of Big Data and Information Engineering Guizhou University Guiyang China

The cold-start problem is a common challenge for most recommender systems. The practical application of most cold-start methods is hindered by the deficiency in auxiliary content information for users. Furthermore, most methods often require simultaneous updates to extensive parameters of recommender models, resulting in high training costs, especially in large-scale industrial scenarios. We observe that the model can generate expressive embeddings for warm users with relatively more interactions. Initially, these users were cold-start users, and after transitioning to warm users, they exhibit clustering patterns in their embeddings with consistent initial interactions. Motivated by this, we propose a Variational Mapping approach for cold-start user Recommendation (VM-Rec), mapping from few initial interactions to expressive embeddings for cold-start users. Specifically, we encode the initial interactions into a latent representation, where each dimension disentangledly signifies the degree of association with each warm user. Subsequently, we utilize this latent representation as the parameters for the mapping function, mapping (decoding) it into an expressive embedding, which can be integrated into a pre-trained recommender model directly. Our method is evaluated on three datasets, demonstrating superior performance compared to other popular cold-start methods.† Copyright © 2023, The Authors. All rights reserved.

关键词： Mapping

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A multi-objective programming model based on the relative deviation 5

A multi-objective programming model based on the relative de...

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2020 5th International Seminar on Computer Technology, Mechanical and Electrical Engineering, ISCME 2020

作者： Wu, Xinlin Xu, Min Department of Mathematics Hubei University of Education Wuhan Hubei430205 China Institute of Big Data Modeling and Intelligent Computing Hubei University of Education Wuhan430205 China

Decision makers are often faced with the achievement of multiple goals in real life. The multi-objective programming model based on priority factors is an effective mathematical model to deal with multiple objectives, but it has some defects. In this paper, the concept of the relative deviation is introduced to deal with the defects of the multi-objective programming model based on priority factors, and the multi-objective programming model based on the relative deviation is proposed. A case study of portfolio decisions is given to illustrate the model. Results show that the model in this paper has more advantages than the traditional model based on priority factors. © Published under licence by IOP Publishing Ltd.

关键词： Multiobjective optimization

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Variational Fuzzy Energy Active Contour Models for Image Segmentation: A Review

Variational Fuzzy Energy Active Contour Models for Image Seg...

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International Conference on Artificial Intelligence and data Sciences (AiDAS)

作者： Tammie Christy Saibin Abdul Kadir bin Jumaat Marina Yusoff Sabah Branch School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Kota Kinabalu Sabah Malaysia School of Mathematical Sciences College of Computing Informatics and Mathematics Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Shah Alam Selangor Malaysia School of Computational Sciences College of Computing Informatics and Mathematics Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Shah Alam Selangor Malaysia

Image segmentation has emerged as one of the main research interests in computer vision over the past decade, which involve many applications, such as medical imaging and biometric identifications. It can be defined as a process of partitioning part of, the whole of, or objects within an image into meaningful regions for further analysis. Image segmentation is divided into variational and non-variational models. Non-variational models are highly dependent on large amounts of data and labels that are not always available and which has led some researchers to focus more on variational models. The most efficient variational model is the active contour model (ACM). However, the main problem faced by variational ACMs is segmenting intensity inhomogeneity images. Hence, this paper presents some strategies that have been utilised in modelling variational ACMs to overcome the problem. One recent strategy is integrating fuzzy theory to help with segmentation. Thus, this paper also highlights a recent type of variational ACM, selective-based variational ACM, that incorporates fuzzy theory in its formulation and discusses the advantages and the challenges of the segmentation model. Finally, several issues and future research directions in variational fuzzy region-based ACM are discussed.

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An Object-Deputy Approach For Chinese Genealogy Collaborative Management 25

An Object-Deputy Approach For Chinese Genealogy Collaborativ...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Jiang, Huan Liu, Honghao Li, Rongrong Peng, Yuwei Xu, Bin Peng, Zhiyong School of Computer Science Wuhan University Wuhan430072 China School of History Wuhan University Wuhan430072 China Big Data Institute Wuhan University Wuhan430072 China Cultural Heritage Intelligent Computing Laboratory Wuhan University Wuhan430072 China

ISBN: (纸本)9781665405270

Chinese genealogy management systems support many functions, such as making genealogy, retrieval and so on. Due to the lack of collaborative management of genealogical data, few systems support genealogical analysis, especially event analysis. In this paper, we propose an approach to manage the Chinese genealogy collaboratively. At first, we analyze the semantics of entities in genealogy. Then we introduce the Object-Deputy mechanism to model Chinese genealogy and implement it in Totem database. Based on this model, we study the common queries and general event relation inference. Additionally, we propose a deep learning model integrating Object Deputy semantics for genealogical event reasoning. Experiments show that our method requires less storage space and achieves better query performance compared with relational method or graph method. Especially, our model integrating the Object-Deputy semantics promotes the accuracy and recall for event reasoning. It proves the effectiveness of our approach. © 2022 IEEE.

关键词： Semantics

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DomainVoyager: Embracing The Unknown Domain by Prompting for Automatic Augmentation

DomainVoyager: Embracing The Unknown Domain by Prompting for...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Chuang Liu Haogang Zhu Xiu Su State Key Laboratory of Software Development Environment Lab Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Zhongfa Aviation Institute Beihang University Hangzhou P.R.China Zhongguancun Laboratory Beijing China Big Data Institute Central South University Changsha China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

For medical image analysis, domain generalization (DG) faces significant challenges due to variances in data across medical imaging devices. Addressing this, we introduce Prompt Guided Domain Aligning Augmentation (PGDAA), a novel approach that harnesses Large Language Models (LLMs) to iteratively refine data augmentation sequences in DG for medical segmentations. Specifically, by harnessing the LLM’s advanced capabilities in interpreting and responding to specific prompts, PGDAA iteratively voyages the parameter searching space for data augmentation, identifying optimal augmentation sequences. In each searching round, the LLM leverages provided prior data augmentation methods, their parameters, and corresponding evaluation results to acquire a sufficient performance memory bank, thereby proposing more effective augmentation sequences to significantly narrowing inter-domain gaps. Notably, this integration of LLMs in DG represents a pioneering application in the field, which adds minor training parameters and can be easily combined with other DG benchmarks for further improvements. Comprehensive experiments reveal our method outperforms the baseline by 4.47% and 5.08% on Fundus and Prostate datasets, achieving 90.10% and 89.28% accuracy, respectively.

关键词： Training Image segmentation Image analysis Accuracy Large language models Benchmark testing data augmentation

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