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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xinyu Zhang Qize Jiang Hanyuan Zhang Weiwei Sun School of Computer Science Shanghai Key Laboratory of Data Science Fudan University Shanghai China Shanghai Institute of Intelligent Electronics & Systems Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The task of next-item recommendation is a crucial component in recommendation systems. The challenge of this task lies in extracting complex interaction information from users’ historical interactions with items. While prior research has transformed users’ interaction histories into graphs and hypergraphs to mine high-order interactions, the integration of both remains uncharted. Existing methods treat graphs and hypergraphs separately in representation learning, missing out on their shared attributes. Addressing this gap, we introduce a novel unified framework that integrates graphs and hypergraphs into one unified message passing paradigm. Our method employs graph neural networks and hypergraph neural networks to continuously conduct representation learning, achieving end-to-end next item recommendation. Specifically, our framework consists of the following components: 1) a unique method for graph and hypergraph construction from interaction histories, with items as nodes and users as hyperedges; 2) a novel message passing framework compatible with both graph and hypergraph neural networks; and 3) an innovative hypergraph neural network aggregation module enhanced with time and position encoders. The test results on multiple public benchmarks verify that our method outperforms current best practices in performance.

关键词： Representation learning Message passing Benchmark testing Signal processing Graph neural networks History data mining Speech processing Recommender systems Best practices

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Instruction-Driven Game Engine: A Poker Case Study

Instruction-Driven Game Engine: A Poker Case Study

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Wu, Hongqiu Liu, Xingyuan Wang, Yan Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China Tencent China

ISBN: (纸本)9798891761674

The Instruction-Driven Game Engine (IDGE) project aims to democratize game development by enabling a large language model (LLM) to follow free-form game descriptions and generate game-play processes. The IDGE allows users to create games simply by natural language instructions, which significantly lowers the barrier for game development. We approach the learning process for IDGEs as a Next State Prediction task, wherein the model autoregressively predicts the game states given player actions. The computation of game states must be precise;otherwise, slight errors could corrupt the game-play experience. This is challenging because of the gap between stability and diversity. To address this, we train the IDGE in a curriculum manner that progressively increases its exposure to complex scenarios. Our initial progress lies in developing an IDGE for Poker, which not only supports a wide range of poker variants but also allows for highly individualized new poker games through natural language inputs. This work lays the groundwork for future advancements in transforming how games are created and played. © 2024 Association for Computational Linguistics.

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A Lightweight Sparse Interaction Network for Time Series Forecasting 39

A Lightweight Sparse Interaction Network for Time Series For...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Xu Wang, Qitong Wang, Peng Wang, Wei Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China Universite Paris Cite Paris France

ISBN: (纸本)157735897X

Recent work shows that linear models can outperform several transformer models in long-term time-series forecasting (TSF). However, instead of explicitly performing temporal interaction through self-attention, linear models implicitly perform it based on stacked MLP structures, which may be insufficient in capturing the complex temporal dependencies and their performance still has potential for improvement. To this end, we propose a Lightweight Sparse Interaction Network (LSINet) for TSF task. Inspired by the sparsity of self-attention, we propose a Multihead Sparse Interaction Mechanism (MSIM). Different from self-attention, MSIM learns the important connections between time steps through sparsity-induced Bernoulli distribution to capture temporal dependencies for TSF. The sparsity is ensured by the proposed self-adaptive regularization loss. Moreover, we observe the shareability of temporal interactions and propose to perform Shared Interaction Learning (SIL) for MSIM to further enhance efficiency and improve convergence. LSINet is a linear model comprising only MLP structures with low overhead and equipped with explicit temporal interaction mechanisms. Extensive experiments on public datasets show that LSINet achieves both higher accuracy and better efficiency than advanced linear models and transformer models in TSF tasks. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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DRL-Based Time-Varying Workload Scheduling With Priority and Resource Awareness

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2838-2852页

作者： Liu, Qifeng Fan, Qilin Zhang, Xu Li, Xiuhua Wang, Kai Xiong, Qingyu Chongqing University School of Big Data and Software Engineering Chongqing400044 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing400044 China Nanjing University School of Electronic Science and Engineering Nanjing210023 China Haihe Laboratory of Information Technology Application Innovation Tianjin300072 China Harbin Institute of Technology School of Computer Science and Technology Weihai264209 China Shandong Key Laboratory of Industrial Network Security Weihai264209 China

With the proliferation of cloud services and the continuous growth in enterprises' demand for dynamic multi-dimensional resources, the implementation of effective strategy for time-varying workload scheduling has become increasingly significant. In this paper, we propose a deep reinforcement learning (DRL)-based method for time-varying workload scheduling, aiming to allocate resources efficiently across servers in the cluster. Specifically, we integrate a classifier and queue scorer to construct a priority queue that exploits temporal resource utilization patterns across different workload classes. Then, we design parallel graph attention layers to capture the dimensional features and temporal dynamics of cloud server cluster. Moreover, we propose a DRL algorithm to generate scheduling strategies that can adapt to dynamic environments. Validation on real-world traces from Google cluster demonstrates that our method outperforms existing approaches in key metrics of cloud server cluster management. © 2004-2012 IEEE.

关键词： Cloud platforms

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Aokana: A GPU-Driven Voxel Rendering Framework for Open World Games

arXiv

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arXiv 2025年

作者： Fang, Yingrong Wang, Qitong Wang, Wei Fudan University Shanghai Key Laboratory of Data Science School of Computer Science Shanghai China Harvard University Cambridge United States

Voxels are among the most popular 3D geometric representations today. Due to their intuitiveness and ease-of-editing, voxels have been widely adopted in stylized games and low-cost independent games. However, the high storage cost of voxels, along with the significant time overhead associated with large-scale voxel rendering, limits the further development of open-world voxel games. In this paper, we introduce Aokana, a GPU-Driven Voxel Rendering Framework for Open World Games. Aokana is based on a Sparse Voxel Directed Acyclic Graph (SVDAG). It incorporates a Level-of-Details (LOD) mechanism and a streaming system, enabling seamless map loading as players traverse the open-world game environment. We also designed a corresponding high-performance GPU-driven voxel rendering pipeline to support real-time rendering of the voxel scenes that contain tens of billions of voxels. Aokana can be directly applied to existing game engines and easily integrated with mesh-based rendering methods, demonstrating its practical applicability in game development. Experimental evaluations show that, with increasing voxel scene resolution, Aokana can reduce memory usage by up to ninefold and achieves rendering speeds up to 4.8 times faster than those of previous state-of-the-art approaches. © 2025, CC BY.

关键词： Rendering (computer graphics)

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Lightweight Storage Framework for Blockchain-Enabled Internet of Things Under Cloud Computing

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computers, Materials & Continua 2023年第5期75卷 3607-3624页

作者： Xinyi Qing Baopeng Ye Yuanquan Shi Tao Li Yuling Chen Lei Liu School of Computer and Artificial Intelligence Huaihua CollegeHuaihua418000China Guizhou Science and Technology Innovation Service Center Co Guiyang550000China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua418000China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550000China

Due to its decentralized,tamper-proof,and trust-free characteristics,blockchain is used in the Internet of Things(IoT)to guarantee the reliability of ***,some technical flaws in blockchain itself prevent the development of these applications,such as the issue with linearly growing storage capacity of blockchain *** the other hand,there is a lack of storage resources for sensor devices in IoT,and numerous sensor devices will generate massive data at ultra-high speed,which makes the storage problem of the IoT enabled by blockchain more *** are various solutions to reduce the storage burden by modifying the blockchain’s storage policy,but most of them do not consider the willingness of *** attempt to make the blockchain more compatible with the IoT,this paper proposes a storage optimization scheme that revisits the system data storage problem from amore practically oriented *** will only store transactional data that they are directly involved *** addition,a transaction verification model is developed to enable peers to undertake transaction verification with the aid of cloud computing,and an incentive mechanism is premised on the storage optimization scheme to assure data *** results of the simulation experiments demonstrate the proposed scheme’s advantage in terms of storage and throughput.

关键词： Blockchain internet of things storage optimization transaction verification cloud computing incentive mechanism

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InsightCube: Accelerating Interactive Insight Discovery in Exploratory data Analysis

InsightCube: Accelerating Interactive Insight Discovery in E...

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2025 IEEE International Conference on Big data and Smart Computing, BigComp 2025

作者： Zhang, Hanbing Jing, Yinan Zhang, Wei He, Zhenying Zhang, Kai Wang, X. Sean Chen, Zhenqiang Zhang, Changchun Fudan University School of Computer Science Shanghai China Shanghai Key Laboratory of Data Science Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Co. Ltd Shanghai China

Exploratory data analysis (EDA) is usually human-in-the-loop and time tedious since identifying insights that show interesting characteristics and trends in data is laborious. Insight discovery helps users gain structured knowledge when understanding the dataset by categorizing interesting characteristics and trends into commonnesses and exceptions. However, existing insight discovery methods cannot interact with users when discovering insights. More importantly, the efficiency of insight discovery for these methods is low. In this paper, we propose a framework that supports and accelerates interactive insight discovery. We first divide the dataset into multiple subsets, and precompute the pattern snippets that include the distribution characteristics of subsets. Considering efficiency, we only pre-compute for subsets that cannot be further split. Then we design a novel cube structure, called InsightCube, to store precomputed pattern snippets. In order to accelerate insight discovery, we reuse the precomputed pattern snippets stored in InsightCube to infer the pattern of subsets that cannot be split. Furthermore, during insight discovery, we propose pruning strategies to accelerate the pattern inference for subsets that can be split. Experimental results on real-world datasets show that InsightCube improves the insight discovery efficiency by up to 2.5× compared to the state-of-the-art insight discovery method. © 2025 IEEE.

关键词： data reduction

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Massively parallel algorithms for fully dynamic all-pairs shortest paths

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Frontiers of computer science 2024年第4期18卷 201-203页

作者： Chilei WANG Qiang-Sheng HUA Hai JIN Chaodong ZHENG National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China

1 Introduction In recent years,the Massively Parallel Computation(MPC)model has gained significant ***,most of distributed and parallel graph algorithms in the MPC model are designed for static graphs[1].In fact,the graphs in the real world are constantly *** size of the real-time changes in these graphs is smaller and more *** graph algorithms[2,3]can deal with graph changes more efficiently[4]than the corresponding static graph ***,most studies on dynamic graph algorithms are limited to the single machine ***,a few parallel dynamic graph algorithms(such as the graph connectivity)in the MPC model[5]have been proposed and shown superiority over their parallel static counterparts.

关键词： dynamic shortest gained

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INCHARACTER: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews 62

INCHARACTER: Evaluating Personality Fidelity in Role-Playing...

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62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024

作者： Wang, Xintao Xiao, Yunze Huang, Jen-Tse Yuan, Siyu Xu, Rui Guo, Haoran Tu, Quan Fei, Yaying Leng, Ziang Wang, Wei Chen, Jiangjie Li, Cheng Xiao, Yanghua Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Carnegie Mellon University United States The Chinese University of Hong Kong Hong Kong School of Data Science Fudan University China RhineAI China Renmin University of China China Beijing University of Technology China Boston University United States SenseTime China

ISBN: (纸本)9798891760943

Role-playing agents (RPAs), powered by large language models, have emerged as a flourishing field of applications. However, a key challenge lies in assessing whether RPAs accurately reproduce the personas of target characters, namely their character fidelity. Existing methods mainly focus on the knowledge and linguistic patterns of characters. This paper, instead, introduces a novel perspective to evaluate the personality fidelity of RPAs with psychological scales. Overcoming drawbacks of previous self-report assessments on RPAs, we propose INCHARACTER, namely Interviewing Character agents for personality tests. Experiments include various types of RPAs and LLMs, covering 32 distinct characters on 14 widely used psychological scales. The results validate the effectiveness of INCHARACTER in measuring RPA personalities. Then, with INCHARACTER, we show that state-of-the-art RPAs exhibit personalities highly aligned with the human-perceived personalities of the characters, achieving an accuracy up to 80.7%. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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KVSAgg: Secure Aggregation of Distributed key-Value Sets 39

KVSAgg: Secure Aggregation of Distributed Key-Value Sets

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39th IEEE International Conference on data Engineering, ICDE 2023

作者： Wu, Yuhan Dong, Siyuan Zhou, Yi Zhao, Yikai Fu, Fangcheng Yang, Tong Niu, Chaoyue Wu, Fan Cui, Bin Peking University School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Beijing China Peng Cheng Laboratory Shenzhen China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9798350322279

In global data analysis, the central server needs the global statistic of the user data stored in local clients. In such cases, an Honest-but-Curious central server might put user privacy at risk in trying to collect individual statistics of each user. In response, the secure aggregation provides a solution for calculating global statistics without revealing users' privacy data. However, existing secure aggregation protocols only focus on the data in the form of vectors or common sets, which limits their application scope. We formalize a general problem - key-value set secure aggregation - that not only includes secure vector aggregation and private set union but also supports more applications. To address the proposed problem, we devise our solution (called the KVSAgg framework) that promises satisfactory performance in security, efficiency, and accuracy. Our key technique is a homomorphic transform algorithm (called HyperIBLT) that is not only capable of bidirectionally transforming data between key-value sets and vectors, but also able to transform sum operation of sets to addition of vectors. We implement KVSAgg on both CPU and GPU platforms and perform the evaluation on three use cases including federated learning, distributed data counting, and finding global hot items. Compared with our baselines, KVSAgg simultaneously achieves the best security, efficiency higher by orders of magnitude, and zero-error in nearly all cases. All codes are open-source anonymously. © 2023 IEEE.

关键词： Vectors

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