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GPS: graph contrastive learning via multi-scale augmented views from adversarial pooling

science China(information sciences) 2025年第1期68卷 145-158页

作者： Wei JU Yiyang GU Zhengyang MAO Ziyue QIAO Yifang QIN Xiao LUO Hui XIONG Ming ZHANG School of Computer Science National Key Laboratory for Multimedia Information ProcessingPeking University Artificial Intelligence Thrust The Hong Kong University of Science and Technology Department of Computer Science University of California

Self-supervised graph representation learning has recently shown considerable promise in a range of fields, including bioinformatics and social networks. A large number of graph contrastive learning approaches have shown promising performance for representation learning on graphs, which train models by maximizing agreement between original graphs and their augmented views(i.e., positive views). Unfortunately, these methods usually involve pre-defined augmentation strategies based on the knowledge of human experts. Moreover, these strategies may fail to generate challenging positive views to provide sufficient supervision signals. In this paper, we present a novel approach named graph pooling contrast(GPS) to address these *** by the fact that graph pooling can adaptively coarsen the graph with the removal of redundancy, we rethink graph pooling and leverage it to automatically generate multi-scale positive views with varying emphasis on providing challenging positives and preserving semantics, i.e., strongly-augmented view and weakly-augmented view. Then, we incorporate both views into a joint contrastive learning framework with similarity learning and consistency learning, where our pooling module is adversarially trained with respect to the encoder for adversarial robustness. Experiments on twelve datasets on both graph classification and transfer learning tasks verify the superiority of the proposed method over its counterparts.

关键词： graph representation learning graph neural networks graph contrastive learning graph augmentations graph pooling

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Robust tube-based MPC with smooth computation for dexterous robot manipulation

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science China(information sciences) 2024年第11期67卷 252-268页

作者： Yu LUO Tianying JI Fuchun SUN Qie SIMA Huaping LIU Mingxuan JING Jianwei ZHANG Department of Computer Science and Technology Tsinghua University Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Department of Informatics University of Hamburg

Dexterous robot manipulation has shone in complex industrial scenarios, where multiple manipulators, or fingers, cooperate to grasp and manipulate objects. When encountering multi-objective optimization with system constraints in such scenarios, model predictive control(MPC) has demonstrated exceptional performance in complex multi-robot manipulation tasks involving multi-objective optimization with system constraints. However, in such scenarios, the substantial computational load required to solve the optimal control problem(OCP) at each triggering instant can lead to significant delays between state sampling and control application, hindering real-time performance. To address these challenges, this paper introduces a novel robust tube-based smooth MPC approach for two fundamental manipulation tasks: reaching a given target and tracking a reference trajectory. By predicting the successor state as the initial condition for imminent OCP solving, we can solve the forthcoming OCP ahead of time, alleviating delay effects. Additionally,we establish an upper bound for linearizing the original nonlinear system, reducing OCP complexity and enhancing response speed. Grounded in tube-based MPC theory, the recursive feasibility and closed-loop stability amidst constraints and disturbances are ensured. Empirical validation is provided through two numerical simulations and two real-world dexterous robot manipulation tasks, which shows that the seamless control input by our methods can effectively enhance the solving efficiency and control performance when compared to conventional time-triggered MPC strategies.

关键词： dexterous robot manipulation model predictive control smooth computation

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Leveraging Spark-TensorFlow Distributor for Distributed Deep Convolutional Neural Networks: Accelerating large-scale COVID-19 Detection

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Informatica (Slovenia) 2025年第17期49卷 173-188页

作者： Mezzoudj, Saliha Khelifa, Meriem Saadna, Yasmina Department of Computer Science University of Algiers Algiers Algeria Artificial Intelligence of Information Technologies Department of Computer Science and Information Technologies University of Kasdi Merbah Ouargla Algeria Labstic laboratory Batna 2 University Batna Algeria

The COVID-19 epidemic has been a critical global challenge due to its high mortality rate and rapid spread. Initial diagnostic methods, such as chest X-rays and reverse transcriptase polymerase chain reaction (RT-PCR), can be time-consuming and require enhanced efficiency, especially in scenarios where large-scale testing is necessary. Combining deep learning (DL) and big data analytics tools is crucial for developing accurate and fast systems to detect COVID-19 infections and prevent further transmission. In this study, we introduce a distributed Deep Convolutional Neural Network (DCNN) framework designed for the detection of COVID-19, utilizing a comprehensive dataset of chest X-ray images. Our approach leverages the spark-tensorflow-distributor, which integrates TensorFlow 2.x with Apache Spark 3.x, enabling distributed training within the Spark framework. The system utilizes Spark’s barrier mode to execute distributed training tasks, significantly enhancing the speed of both training and testing phases. We classify chest X-ray images into two categories: COVID-19 and non-COVID-19. The experimental setup involves processing the large-scale dataset of chest X-ray images in a parallelized manner using the Spark framework. Our results demonstrate that the proposed DCNN model achieves a classification accuracy of 95.53%, with a testing time of 0.06 seconds and a training time of 18,909 seconds. Furthermore, when compared to other state-of-the-art methods for COVID-19 classification, our approach proves to be more efficient and effective. This study highlights the potential of combining distributed computing frameworks with deep learning techniques to address largescale medical image analysis challenges, particularly in the context of COVID-19 detection. © 2025 Slovene Society Informatika. All rights reserved.

关键词： COVID-19

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Committed-programming reductions: formalizations,implications and relations

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science China(information sciences) 2024年第10期67卷 151-171页

作者： Jiang ZHANG Yu YU Dengguo FENG Shuqin FAN Zhenfeng ZHANG State Key Laboratory of Cryptology Department of Computer Science and Engineering Shanghai Jiao Tong University Trusted Computing and Information Assurance Laboratory Institute of SoftwareChinese Academy of Sciences

In this work, we introduce a class of black-box(BB) reductions called committed-programming reduction(CPRed) in the random oracle model(ROM) and obtain the following interesting results:(1) we demonstrate that some well-known schemes, including the full-domain hash(FDH) signature(Eurocrypt1996) and the Boneh-Franklin identity-based encryption(IBE) scheme(Crypto 2001), are provably secure under CPReds;(2) we prove that a CPRed associated with an instance-extraction algorithm implies a reduction in the quantum ROM(QROM). This unifies several recent results, including the security of the Gentry-Peikert-Vaikuntanathan IBE scheme by Zhandry(Crypto 2012) and the key encapsulation mechanism(KEM) variants using the Fujisaki-Okamoto transform by Jiang et al.(Crypto 2018) in the ***, we show that CPReds are incomparable to non-programming reductions(NPReds) and randomly-programming reductions(RPReds) formalized by Fischlin et al.(Asiacrypt 2010).

关键词： provable security random oracle model quantum random oracle model black-box reduction/separation programmability

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Predicting Age and Gender in Author Profiling: A Multi-Feature Exploration

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computers, Materials & Continua 2024年第5期79卷 3333-3353页

作者： Aiman Muhammad Arshad Bilal Khan Sadique Ahmad Muhammad Asim Department of Computer Science City University of Science and Information TechnologyPeshawar25000Pakistan EIAS:Data Science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia School of Computer Science and Technology Guangdong University of TechnologyGuangzhou510006China

Author Profiling (AP) is a subsection of digital forensics that focuses on the detection of the author’s personalinformation, such as age, gender, occupation, and education, based on various linguistic features, e.g., stylistic,semantic, and syntactic. The importance of AP lies in various fields, including forensics, security, medicine, andmarketing. In previous studies, many works have been done using different languages, e.g., English, Arabic, French,***, the research on RomanUrdu is not up to the ***, this study focuses on detecting the author’sage and gender based on Roman Urdu text messages. The dataset used in this study is Fire’18-MaponSMS. Thisstudy proposed an ensemble model based on AdaBoostM1 and Random Forest (AMBRF) for AP using multiplelinguistic features that are stylistic, character-based, word-based, and sentence-based. The proposed model iscontrasted with several of the well-known models fromthe literature, including J48-Decision Tree (J48),Na飗e Bays(NB), K Nearest Neighbor (KNN), and Composite Hypercube on Random Projection (CHIRP), NB-Updatable,RF, and AdaboostM1. The overall outcome shows the better performance of the proposed AdaboostM1 withRandom Forest (ABMRF) with an accuracy of 54.2857% for age prediction and 71.1429% for gender predictioncalculated on stylistic features. Regarding word-based features, age and gender were considered in 50.5714% and60%, respectively. On the other hand, KNN and CHIRP show the weakest performance using all the linguisticfeatures for age and gender prediction.

关键词： Digital forensics author profiling for security AdaBoostM1 random forest ensemble learning

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MMInstruct: a high-quality multi-modal instruction tuning dataset with extensive diversity

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science China(information sciences) 2024年第12期67卷 36-51页

作者： Yangzhou LIU Yue CAO Zhangwei GAO Weiyun WANG Zhe CHEN Wenhai WANG Hao TIAN Lewei LU Xizhou ZHU Tong LU Yu QIAO Jifeng DAI School of Computer Science Nanjing University School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai AI Laboratory School of Computer Science Fudan University Department of Information Engineering The Chinese University of Hong Kong SenseTime Research Department of Electronic Engineering Tsinghua University

Despite the effectiveness of vision-language supervised fine-tuning in enhancing the performance of vision large language models(VLLMs), existing visual instruction tuning datasets include the following limitations.(1) Instruction annotation quality: despite existing VLLMs exhibiting strong performance,instructions generated by those advanced VLLMs may still suffer from inaccuracies, such as hallucinations.(2) Instructions and image diversity: the limited range of instruction types and the lack of diversity in image data may impact the model's ability to generate diversified and closer to real-world scenarios outputs. To address these challenges, we construct a high-quality, diverse visual instruction tuning dataset MMInstruct,which consists of 973k instructions from 24 domains. There are four instruction types: judgment, multiplechoice, long visual question answering, and short visual question answering. To construct MMInstruct, we propose an instruction generation data engine that leverages GPT-4V, GPT-3.5, and manual correction. Our instruction generation engine enables semi-automatic, low-cost, and multi-domain instruction generation at 1/6 the cost of manual construction. Through extensive experiment validation and ablation experiments,we demonstrate that MMInstruct could significantly improve the performance of VLLMs, e.g., the model fine-tuning on MMInstruct achieves new state-of-the-art performance on 10 out of 12 benchmarks. The code and data shall be available at https://***/yuecao0119/MMInstruct.

关键词： instruction tuning multi-modal multi-domain dataset vision large language model

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Fixed-point quantum continuous search algorithm with optimal query complexity

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science China(Physics,Mechanics & Astronomy) 2025年第6期Mechanics & Astronomy) .卷 84-95页

作者： Shan Jin Yuhan Huang Shaojun Wu Guanyu Zhou Chang-Ling Zou Luyan Sun Xiaoting Wang Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Key Laboratory of Quantum Information CAS University of Science and Technology of China Hefei National Laboratory Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University

Continuous search problems(CSPs), which involve finding solutions within a continuous domain, frequently arise in fields such as optimization, physics, and engineering. Unlike discrete search problems, CSPs require navigating an uncountably infinite space, presenting unique computational challenges. In this work, we propose a fixed-point quantum search algorithm that leverages continuous variables to address these challenges, achieving a quadratic speedup. Inspired by the discrete search results, we manage to establish a lower bound on the query complexity of arbitrary quantum search for CSPs, demonstrating the optimality of our approach. In addition, we demonstrate how to design the internal structure of the quantum search oracle for specific problems. Furthermore, we develop a general framework to apply this algorithm to a range of problem types, including optimization and eigenvalue problems involving continuous variables.

关键词： quantum search quantum algorithm quantum computation optimization spectral problems

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Enhanced VR Experience with Edge Computing: The Impact of Decoding Latency

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IEEE Transactions on Mobile Computing 2025年第07期24卷 6275-6292页

作者： Huang, Liang Li, Yuqi Liang, Hongyuan Chi, Kaikai Wu, Yuan Zhejiang University of Technology School of Computer Science and Technology China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science Macau China

Virtual reality (VR) applications have revolutionized digital interaction by providing immersive experiences.360° VR video streaming has experienced significant growth and popularity as a pivotal VR application. However, the combination of limited network bandwidth and the demand for high-quality videos frequently hinders the achievement of a satisfactory quality of experience (QoE). Although prior methods have enhanced QoE, the effects of decoding latency have been poorly studied. It is technically challenging to design a quality adaptation algorithm that can balance the pursuit of high-quality videos and the limitation of limited bandwidth resources. To address this challenge, we propose an edge-end architecture for 360° VR video streaming and aim to enhance overall QoE by solving a performance optimization problem. Specifically, our experiments on commercial mobile devices in real-world situations reveal that decoding latency significantly influences QoE. First, decoding latency plays a major role in contributing to end-to-end latency, which exceeds the transmission latency. Second, decoding latency can differ considerably between devices with varying computational capabilities. Building on this insight, we propose a novel latency-aware quality adaptation (LAQA) algorithm. LAQA lies in developing a solution that can allocate video quality in real-time and enhance overall QoE. LAQA involves not only the quality of the received content, the transmission latency and the quality variance, but also the decoding latency and the fairness of the user quality. Subsequently, we formulate a combinatorial optimization problem to maximize overall QoE. Through extensive validation with experimental data from real-world situations, LAQA offers a promising approach to enhance QoE and ensure fairness performance in different devices. In particular, LAQA achieves 16.77% and 10.66% enhancement over the state-of-the-art combinatorial optimization and reinforcement learning algorithm

关键词： Bandwidth

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Probing quantum causality with geometric asymmetry in spatial-temporal correlations

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science China(information sciences) 2024年第9期67卷 294-305页

作者： Yu MENG Zheng-Hao LIU Zhikuan ZHAO Peng YIN Yi-Tao WANG Wei LIU Zhi-Peng LI Yuan-Ze YANG Zhao-An WANG Jin-Shi XU Shang YU Jian-Shun TANG Chuan-Feng LI Guang-Can GUO CAS Key Laboratory of Quantum Information University of Science and Technology of China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Department of Computer Science Eidgen?ssische Technische Hochschule Zürich Hefei National Laboratory Department of Physics Imperial College London Research Center for Quantum Sensing Zhejiang Lab

Causation promotes the understanding of correlation to an advanced stage by elucidating its underlying mechanism. Although statisticians have specified the possible causal relations among correlations,inferring causal structures is impossible from only the observed correlations in the classical world. Quantum correlations encapsulating the most defining aspects of quantum physics have taken a new turn for the causal inference problem — the two-point spatial and temporal quantum correlations with observationally discernible characteristics correspond exactly to the two most basic causal structures. However, a direct causal interpretation for quantum correlations has only been established in very limited cases. Here, we explore to what extent quantum correlations promote causal inference. Theoretically, we have found that the distinguishable causal regime of two-point Pauli correlations can be expanded from a single value to an asymmetric interval, and the causal structures determining the quantum correlations can be interpreted by a simple distance criterion. Experimentally, we have devised and implemented a versatile non-unital quantum channel in an optical architecture to directly observe such an asymmetric interval. The setup enabled quantum causal inference without any requirement of active intervention, which is impossible in the classical realm. Our work facilitates the identification of causal links among quantum variables and provides insight into characterizing causation and spatial-temporal correlation in quantum mechanics.

关键词： causality quantum information quantum channel channel-state duality non-unital channel

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Optimized Autonomous Computing with Trusted Resilient Data Management for IoT Emerging Networks

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IEEE Sensors Journal 2025年第11期25卷 20778-20785页

作者： Shafique, Ayesha Cheng, Benmao Zhao, Bo Alwakid, Ghadah Naif Haq, Muhammad Inaam Ul Laboratory of Intelligent IOT Technology and Applications in Universities China Jouf University College of Computer and Information Sciences Department of Computer Science Al Jowf Sakaka72341 Saudi Arabia Quanzhou University of Information Engineering School of Computer Science and Software Engineering Quanzhou362000 China

The innovative city network integrates numerous computational and physical components to develop real-time systems. These systems can capture sensor data and distribute it to end stations. Most solutions have been presented based on the centralized computing paradigm, which effectively and systematically increases data flow;however, distributed wireless technologies and heterogeneous network services continue to raise significant research problems. These challenges lower the optimization criteria and reflect communication structure around the network edges. In this research, we proposed a sustainable development for smart networks using efficient big data management with collaborative decisions for network devices. It differs from most existing work in the mentioned aspect. It applies computational lightweight intelligence for forwarding collected data using mobile collectors and reduces the congestion flow between devices on the limited bandwidth of wireless links. Moreover, the energy load is efficiently managed with edge-driven methods, and the incorporation of trusted devices ensures the integrity of the smart network. It also tackles potential communication threats smartly. Based on the experiments conducted in Network Simulator (NS-3), the proposed model enhances the efficacy of smart networks for performance metrics with reliability and effective management of resources in Internet of Things (IoT) network. © 2025 IEEE.

关键词： Mathematical models Computational modeling Intelligent sensors Data aggregation Internet of Things Security Routing Load modeling Silicon Data models

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