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arXiv 2024年

作者： Pan, Yinghui Ma, Biyang Zhang, Hanyi Zeng, Yifeng National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Computer Science Minnan Normal University Zhangzhou China Department of Computer and Information Sciences Northumbria University Newcastle United Kingdom

Addressing multiagent decision problems in AI, especially those involving collaborative or competitive agents acting concurrently in a partially observable and stochastic environment, remains a formidable challenge. While Interactive Dynamic Influence Diagrams (I-DIDs) have offered a promising decision framework for such problems, they encounter limitations when the subject agent encounters unknown behaviors exhibited by other agents that are not explicitly modeled within the I-DID. This can lead to sub-optimal responses from the subject agent. In this paper, we propose a novel data-driven approach that utilizes an encoder-decoder architecture, particularly a variational autoencoder, to enhance I-DID solutions. By integrating a perplexity-based tree loss function into the optimization algorithm of the variational autoencoder, coupled with the advantages of Zig-Zag One-Hot encoding and decoding, we generate potential behaviors of other agents within the I-DID that are more likely to contain their true behaviors, even from limited interactions. This new approach enables the subject agent to respond more appropriately to unknown behaviors, thus improving its decision quality. We empirically demonstrate the effectiveness of the proposed approach in two well-established problem domains, highlighting its potential for handling multi-agent decision problems with unknown behaviors. This work is the first time of using neural networks based approaches to deal with the I-DID challenge in agent planning and learning problems. © 2024, CC BY.

关键词： Decoding

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Advanced insulating materials contributing to“carbon neutrality”:Opportunities,issues and challenges

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High Voltage 2022年第4期7卷 607-609页

作者： Chuanyang Li Yunlong Zi Yang Cao Giovanni Mazzanti Davide Fabiani Di Zhang Yunqi Xing Jinliang He State Key Laboratory of Power System Department of Electrical EngineeringTsinghua UniversityBeijing100084China Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and TechnologyClear Water BayHong Kong SARChina Electrical and Computer Engineering University of Connecticut371 Fairfield WayStorrsCT 06269USA Department Electrical Electronic and Information EngineeringUniversity of BolognaBologna 40136Italy Electrical and Computer Engineering Department Naval Postgraduate School1 University CircleMontereyCA 93943USA State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of TechnologyTianjin300131China

The vision of 2050 worldwide carbon-neutral ignites a revo-lution in all *** innovations in both electrical power equipment and electrified transportation assets are shaping our future energy *** materials,as the core element of energy infrastructure,are facing unprece-dented challenges that stand in the pathways to global carbon ***,researchers from both academic in-stitutions and industrial sections are working to turn the challenges into opportunities.

关键词： structure. neutral carbon

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Sasirangan Cloth Recognition and Shopping Experience Simulation Based on Virtual Reality Game with Non-Player Character Integration 8

Sasirangan Cloth Recognition and Shopping Experience Simulat...

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8th International HCI and UX Conference in Indonesia, CHIuXiD 2022

作者： Subandi, Subandi Syahidi, Aulia Akhrian Zakiah, Siti Kiyokawa, Kiyoshi Riyadi, Ahmad Noor, M. Helmy Politeknik Negeri Banjarmasin Smart City Information System Applied Undergraduate Program Banjarmasin Indonesia Universitas Brawijaya Informatics Engineering Undergraduate Program Faculty of Computer Science Malang Indonesia Nara Institute of Science and Technology Cybernetics and Reality Engineering Laboratory Ikoma Japan Politeknik Negeri Banjarmasin Informatics Engineering Diploma Program Banjarmasin Indonesia

ISBN: (纸本)9781665476645

Sasirangan cloth is one of the traditional cloths owned by Indonesia and is a typical cloth originating from the province of South Kalimantan. This Sasirangan cloth has many motifs and is unique. Sasirangan cloth is also one of the potentials that can attract local and foreign tourists to visit South Kalimantan. However, in recent years, tourist visits to South Kalimantan have decreased due to the impact of the COVID-19 pandemic. Then it also has an impact on the delay in the implementation of the Banjarmasin Sasirangan Festival which is usually held annually. The next impact is a decrease in turnover and interest in shopping for Sasirangan cloth, so that employees who work for Sasirangan cloth craftsmen are dismissed and reduce people's income. So the purpose of this study is to report the results of the development of a Virtual Reality Game application called VR-SasiranganKu by integrating the interaction of Non-Player Character (NPC) behavior with the type of narrative in the simulation genre game. VR-SasiranganKu is recommended to introduce and at the same time promote Sasirangan cloth to users, then justify the reaction to knowledge and shopping experience, and evaluate user experience using the UX Honeycomb method. Respondents involved as many as 100 people who are local and national tourists. The results show that the interaction of NPC behavior can provide good direction to users regarding the introduction of Sasirangan cloth by presenting an appropriate interaction design based on the narrative and the theme of the NPC that has been adopted, the results of justification using VR-SasiranganKu can provide knowledge reaction and very good shopping experience, and based on user experience evaluation that VR-SasiranganKu has adopted a very good UX with an average score of 4.91. © 2022 IEEE.

关键词： Virtual reality

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CtrlFuzz: A Controllable Diffusion-Based Fuzz Testing for Deep Neural Networks Via Coverage-Aware Manifold Guidance

SSRN

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SSRN 2025年

作者： Ye, Aoshuang Zhang, Shilin Yan, Runze Ke, Jianpeng Zhu, Fei Tang, Benxiao School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan430200 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Sichuan China School of Cyber Science and Engineering Wuhan University Wuhan430072 China

Context: Deep neural networks (DNNs) have been extensively deployed in safety-critical applications. Nevertheless, the inherent vulnerability to subtle perturbations of inputs constitutes serious risks to the reliability of DNN-based systems. While mutation-based coverage-guided fuzzing (CGF) ensures test oracle through deliberately limited perturbations, it struggles to obtain diverse and sparse test cases. Conversely, generation-based CGF is able to create more diverse test cases aligned with data distribution but lacks precise ***: To refine the controllability and effectiveness of CGF in DNN testing, we aim to design a framework that is capable of generating realistic test cases with fine-grained control, while systematically exploring model vulnerabilities through a manifold-aware coverage ***: In this paper, we propose CtrlFuzz, a manifold coverage-guided controllable diffusion framework for testing DNNs. CtrlFuzz leverages manifold learning to embed high-dimensional inputs into a lower-dimensional Euclidean space, preserving geometric structure. Based on this, we define a manifold coverage by quantifying the ratio between the distances from seed and the non-adversarial counterparts to class center. We further enhance the testing controllability via performing semantic decomposition on seed inputs. A customized diffusion model based on the U-Net structure integrates manifold coverage and semantic constraints into the denoising process, which allows to remain semantically natural while covering vulnerable ***: Experimental results on four popular datasets and ten benchmark DNN architectures demonstrate that CtrlFuzz (1) effectively maintains the semantic coherence of generated test cases, (2) achieves improved exploration of vulnerable manifold regions compared to existing CGF techniques, and (3) discovers significantly more error-inducing inputs on multiple model ***: CtrlFuzz introduces a novel manif

关键词： Controllability

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Ant: An Efficient Lossless Compression Algorithm for IoT Time Series Data

Ant: An Efficient Lossless Compression Algorithm for IoT Tim...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Junhui Li Guangping Xu Hongzhang Yang Yulei Wu School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education China Faculty of Science and Engineering University of Bristol Bristol U.K

In various applications in Internet of Things like industrial monitoring, large amounts of floating-point time series data are generated at an unprecedented rate. Efficient compression algorithms can effectively reduce the size of data, enhance transmission performance and storage efficiency, and simultaneously lower storage costs. Therefore, there is a need for lightweight and efficient stream compression algorithms. In this paper, we propose a novel lossless floating-point data compression algorithm called Ant. The main idea is to encode double-precision floating-point numbers into integer form, calculate the delta between adjacent values, and then convert the delta into unsigned integers. This encoding method effectively reduces storage costs and improves data compression efficiency. Extensive experiments on real-world datasets demonstrate that our algorithm achieves compression speeds at least as fast as state-of-the-art streaming methods, and a 63% relative improvement in average compression rate.

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Generative Positive-Unlabeled Classification for Hunting Small Open Reading Frames

Generative Positive-Unlabeled Classification for Hunting Sma...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jie Chen Wenbin Liao Du Wen Jianqiang Li Fangzhong Wang College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Laboratory of Synthetic Microbiology School of Chemical Engineering & Technology Tianjin University China

The annotation of Open Reading Frames (ORFs) is a crucial step in gene annotation, as it precisely delineates the specific regions of expressed genes. However, small Open Reading Frames (smORFs), in comparison to ORFs, are shorter in length, exhibit lower expression abundance, and are more challenging to predict. Particularly in the presence of noise in prokaryotic data and limited availability of positive sample data, the difficulty of prediction is amplified. Therefore, it is necessary to study smORF prediction methods. However, current machine learning models use limited data for modeling and overlook the existence of undiscovered positive samples within the negative samples. Additionally, they do not incorporate prior knowledge that can be calibrated to enhance the 3-nt periodicity. This work utilizes a multimodal VAE for data dimensionality reduction and employs a GAN to generate latent vectors for data augmentation. It incorporates PU learning to leverage unknown samples and combines Riboseq data from experiments with and without antibiotic treatment. Additionally, an adversarial training mechanism is employed to enhance the model’s robustness.

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Reinforcement Learning-based Token Pruning in Vision Transformers: A Markov Game Approach

arXiv

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arXiv 2025年

作者： Lu, Chenglong Liang, Shen Wang, Xuewei Wang, Wei School of Computer Science Fudan University China LIPADE Université Paris Cité Paris France State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Shijiazhuang Tiedao University Shijiazhuang China

Vision Transformers (ViTs) have computational costs scaling quadratically with the number of tokens, calling for effective token pruning policies. Most existing policies are handcrafted, lacking adaptivity to varying inputs. Moreover, they fail to consider the sequential nature of token pruning across multiple layers. In this work, for the first time (as far as we know), we exploit Reinforcement Learning (RL) to data-adaptively learn a pruning policy. Formulating token pruning as a sequential decision-making problem, we model it as a Markov Game and utilize Multi-Agent Proximal Policy Optimization (MAPPO) where each agent makes an individualized pruning decision for a single token. We also develop reward functions that enable simultaneous collaboration and competition of these agents to balance efficiency and accuracy. On the well-known ImageNet-1k dataset, our method improves the inference speed by up to 44% while incurring only a negligible accuracy drop of 0.4%. The source code is available at https://***/daashuai/rl4evit. © 2025, CC BY.

关键词： Reinforcement learning

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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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Generating Highly Structured Inputs: A Survey 6

Generating Highly Structured Inputs: A Survey

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6th IEEE International Conference on Data science in Cyberspace, DSC 2021

作者： Li, Yabin Qin, Yinfan Tian, Jianwen Gu, Taotao Nie, Yuanping Kuang, Xiaohui School Of Computer Science Beijing University Of Posts And Telecommunications Beijing China Institute Of System Engineering Academy Of Military Sciences Beijing China National Key Laboratory Of Science And Technology On Information System Security Beijing China

ISBN: (纸本)9781665418157

Fuzzing is currently one of the most popular vulnerability mining technology. The test case generation technique plays a key role in fuzzing, and the quality of the generated test cases directly affects the effect of fuzzing. Some programs process highly structured inputs which need to satisfy clear syntactic and semantic constraints, but traditional fuzzers (such as AFL) are difficult to generate such inputs. In this paper, we focus on the techniques of generating highly structured inputs. We first systematically introduce the mainstream techniques of generating highly structured inputs, then discuss the key issues facing the current technique and main technique trends, finally summarize the relevant open-source tools to promote future research. © 2021 IEEE.

关键词： Semantics

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OneForecast: A Universal Framework for Global and Regional Weather Forecasting

arXiv

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arXiv 2025年

作者： Gao, Yuan Wu, Hao Shu, Ruiqi Dong, Huanshuo Xu, Fan Chen, Rui Yan, Yibo Wen, Qingsong Hu, Xuming Wang, Kun Wu, Jiahao Li, Qing Xiong, Hui Huang, Xiaomeng Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University China TEG Tencent China Department and Computer and Science University of Science and Technology of China China Institute for Interdisciplinary Information Sciences Tsinghua University China Department of Computer Science and Engineering The HongKong University of Science and Technology Hong Kong AI Thrust The Hong Kong University of Science and Technology Guangzhou China Squirrel Ai Learning China School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computing The Hong Kong Polytechnic University Hong Kong

Accurate weather forecasts are important for disaster prevention, agricultural planning, and water resource management. Traditional numerical weather prediction (NWP) methods offer physically interpretable high-accuracy predictions but are computationally expensive and fail to fully leverage rapidly growing historical data. In recent years, deep learning methods have made significant progress in weather forecasting, but challenges remain, such as balancing global and regional high-resolution forecasts, excessive smoothing in extreme event predictions, and insufficient dynamic system modeling. To address these issues, this paper proposes a global-regional nested weather forecasting framework based on graph neural networks (GNNs). By combining a dynamic system perspective with multi-grid theory, we construct a multi-scale graph structure and densify the target region to capture local high-frequency features. We introduce an adaptive information propagation mechanism, using dynamic gating units to deeply integrate node and edge features for more accurate extreme event forecasting. For high-resolution regional forecasts, we propose a neural nested grid method to mitigate boundary information loss. Experimental results show that the proposed method performs excellently across global to regional scales and short-term to long-term forecasts, especially in extreme event predictions (e.g., typhoons), significantly improving forecast accuracy. Our codes are available at https://***/YuanGao-YG/OneForecast. Copyright © 2025, The Authors. All rights reserved.

关键词： Weather forecasting

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