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arXiv 2024年

作者： Dong, Qingxiu Zhou, Guangyan Xu, Ke School of Computer Science Peking University China Department of Mathematics and Statistics Beijing Technology and Business University China State Key Lab of Software Development Environment Beihang University China

Recently, Xu and Zhou [2023] introduced a constructive approach for exploring computational hardness, proving that SAT requires exhaustive search. In light of certain misinterpretations concerning the contributions and proofs in that paper, we focus on providing detailed explanations in this work. We begin by delineating the core innovation of the constructive approach, shedding light on the pivotal concept of algorithm designability. We address the overlooked white-box diagonalization method and highlight the concept of an almost independent solution space. In response to specific misunderstandings, such as the concerns surrounding the assumptions of Lemma 3.1, we offer comprehensive clarifications aimed at improving the comprehension of the proof. We are grateful for the feedback received on our prior paper and hope this work can foster a more well-informed discussion. Copyright © 2024, The Authors. All rights reserved.

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Crawling and Exploring RESTful Web APIs from RapidAPI 17th

Crawling and Exploring RESTful Web APIs from RapidAPI

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CCF 17th International Conference on Service Science, CCF ICSS 2024

作者： Li, Wen Ren, Hongshuai Nie, Yamei Liu, Zihao Kang, Guosheng Liu, Jianxun Peng, Zhenlian School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology HNUST Xiangtan China

ISBN: (纸本)9789819757596

RapidAPI serves as a marketplace and management platform that integrates a wide range of Web APIs, offering developers access to a rich pool of API resources. More and more researchers are delving into ways to maximize the value created by leveraging these resources. Thus, acquiring the Web API data and knowing the details is of significance for researchers and ordinary users. In this paper, we propose the methodology of crawling Web APIs from RapidAPI (https://***/hub) and provide rich visualizations and statistical analysis for the crawled dataset. The crawling code and the crawled dataset are open for public access (https://***/IntelligentServicelab/RESTful-API-Crawler) so that researchers and users can acquire them with no effort. Moreover, the use cases of RESTful Web APIs are provided as a usage reference for users and developers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Web crawler

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Multi-view Hypergraph-based Self-supervised Learning Model for Web API Recommendation

Multi-view Hypergraph-based Self-supervised Learning Model f...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Jiexun Shen Dongfan Li Wanjun Chen Yong Xiao Guosheng Kang Jianxun Liu Zhenlian Peng School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology HNUST Xiangtan China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

With the rapid development of service computing technology, how to recommend the desirable Web APIs to developers from the large number of APIs is a challenge. The traditional methods based on collaborative filtering are limited by data sparsity. With the support of multi-dimensional relational feature modeling, graph neural network-based methods are proposed to mitigate data sparsity, but their convolution nature may amplify the noise effect. Therefore, how to simultaneously reduce the impact of sparsity and noisy data has been an open question in the field of API recommendation. To address the problem, this paper proposes a self-supervised learning method based on multi-view hypergraph for Web API recommendation. First, the Mashup-API interaction graph is transformed into a hypergraph, and the hyperedges are used as intermediate hubs to transfer messages between nodes, maintaining the global collaboration effect between Mashup and API nodes. Then, a multi-view strategy is adopted to generate embeddings of Mashups and APIs through information fusion, by which recommendation probability is derived by dot product between the embeddings of Mashups and Web APIs. To train the model parameters effectively, a self-supervised learning method is used to reduce the effect of noisy data to improve the embeddings. Extensive experiments are conducted on a real-world dataset, and the experimental results show that the proposed model outperforms the baselines.

关键词： Knowledge engineering Mashups Noise Neural networks Collaboration Service computing Self-supervised learning Feature extraction Data models Noise measurement

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Online Course Recommendation by Exploring User Interaction and Course Description

Online Course Recommendation by Exploring User Interaction a...

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International Conference on computer Supported Cooperative Work in Design

作者： Weike Zhou Xiaokang Zhou Jing He Yamei Nie Qiqi Chen Guosheng Kang School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology HNUST Xiangtan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

With the wide use of Massive Open Online Courses (MOOCs) and online education, the large number and variety of educational resources on the online course open platform make it difficult for users to choose, and the requirements for course recommendation algorithms and model performance are getting higher and higher. The existing course recommendation algorithms often ignore the interactive information between n learners and courses and the course descriptions, resulting in poor recommendation performance. To solve the problems above, our work proposes an online course recommendation approach via exploring user interactions and course description documents. Specifically, LightGCN model is used to represent the user-course interaction graph to derive the structural embeddings of users and courses. Moreover, BERT model is used to represent the course description documents to derive the description embeddings of courses. Based on the three embeddings, ANFM (Attention Neural Factorization Machine) model is applied to perform online course recommendation by modeling low-order and high-order feature interactions, and learning the weights of the feature interactions through the attention mechanism. Finally, solid experiments are conducted over a real-world public dataset. Experimental results show that the proposed approach outperforms the baseline methods under NDCG, Recall and Precision.

关键词： Electronic learning Attention mechanisms Federated learning Education Solids

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NaviFormer: A Spatio-Temporal Context-Aware Transformer for Object Navigation 39

NaviFormer: A Spatio-Temporal Context-Aware Transformer for ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xie, Wei Jiang, Haobo Zhu, Yun Qian, Jianjun Xie, Jin PCA Lab School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Nanyang Technological University Singapore State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Intelligence Science and Technology Nanjing University Suzhou China

ISBN: (纸本)157735897X

Learning discriminative state representations of agents, encompassing the spatial layout and temporal pose trajectory, is essential for effective navigation decisions. However, existing approaches often rely on simplistic plain networks for navigation information fusion, overlooking the complex long-range dependencies across spatio-temporal cues, which leads to suboptimal state perception and potential decision failures. In this paper, we introduce NaviFormer, an effective encoder-decoder navigation transformer, to aggregate discriminative spatio-temporal context information for object navigation. Our navigation encoder not only encodes spatial layouts and temporal agent poses but also innovatively constructs and encodes a passable frontier map, enriching the original state encoding with cues of potential exploration regions. Furthermore, our navigation decoder employs spatio-temporal self-attention and cross-attention mechanisms to model the dependencies among spatial layout encoding, temporal pose encoding, and passable frontier encoding, thereby facilitating comprehensive contextual state feature aggregation. Finally, we leverage these learned spatio-temporal contextual state representations for PPO-based navigation decisions. Extensive experiments on the Gibson, Habitat-Matterport3D (HM3D) and Matterport3D (MP3D) datasets demonstrate the superiority of our approach. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Network coding

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Micro-macro Wavelet-based Gaussian Splatting for 3D Reconstruction from Unconstrained Images

arXiv

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arXiv 2025年

作者： Li, Yihui Lv, Chengxin Yang, Hongyu Huang, Di State Key Laboratory of Complex and Critical Software Environment Beijing China School of Computer Science and Engineering Beihang University China School of Artificial Intelligence Beihang University China Shanghai Artificial Intelligence Laboratory Shanghai China

3D reconstruction from unconstrained image collections presents substantial challenges due to varying appearances and transient occlusions. In this paper, we introduce Micro-macro Wavelet-based Gaussian Splatting (MW-GS), a novel approach designed to enhance 3D reconstruction by disentangling scene representations into global, refined, and intrinsic components. The proposed method features two key innovations: Micro-macro Projection, which allows Gaussian points to capture details from feature maps across multiple scales with enhanced diversity;and Wavelet-based Sampling, which leverages frequency domain information to refine feature representations and significantly improve the modeling of scene appearances. Additionally, we incorporate a Hierarchical Residual Fusion Network to seamlessly integrate these features. Extensive experiments demonstrate that MW-GS delivers state-of-the-art rendering performance, surpassing existing methods. Copyright © 2025, The Authors. All rights reserved.

关键词： 3D reconstruction

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A Dynamic Load Balancing Algorithm for Stable Pushing of Docker Images in Nginx

A Dynamic Load Balancing Algorithm for Stable Pushing of Doc...

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International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI)

作者： Hajie Xi Song Hu He Zhang Shuang Zhang Jingnong Weng Jian Huang School of Computer Science and Engineering Beihang University Beijing China State Key Lab of Transportation System Research Institute of Highway Ministry of Transport Beijing China Transportation Comprehensive Enforcement Corps Beijing China School of Software Beihang University Beijing China

ISBN: (数字)9798331541798

ISBN: (纸本)9798331505615

With the rapid development of artificial intelligence, a wide variety of deep learning models have emerged across various fields. Nevertheless, the quality of these models varies significantly, making the effective evaluation of model performance a pressing issue. In some open data competitions, remote evaluation based on Docker images has increasingly become a novel and reliable approach, providing a consistent environment to ensure the fairness of model evaluations. However, it imposes high demands on hardware resources, typically requiring dedicated resources for each participant to serve as Docker Registries for the pushing and management of Docker images. In this paper, we extend the load balancing module of the Nginx server by integrating Lua scripts and propose a Cache-based Dynamic Smooth Weighted Round-robin (CDSWRR) algorithm to optimize the pushing process of Docker images. By constructing a unified load balancing entry, the proposed approach simplifies the deployment of Docker Registry clusters. Experiments show that compared to weighted round-robin and IP hash algorithms of Nginx, the CDSWRR algorithm is able to ensure stable pushing of Docker images and reduce both response error rate and response time.

关键词： Machine learning algorithms Error analysis Heuristic algorithms Clustering algorithms Pressing Load management Time factors Servers Reliability Open data

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Fully-Scalable Massively Parallel Algorithm for k-center with Outliers 39

Fully-Scalable Massively Parallel Algorithm for k-center wit...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wu, Di Feng, Qilong Huang, Junyu Xu, Jinhui Huang, Ziyun Wang, Jianxin School of Computer Science and Engineering Central South University Changsha 410083 China Xiangjiang Laboratory Changsha 410205 China Department of Computer Science and Engineering State University of New York Buffalo NY United States Department of Computer Science and Software Engineering Penn State Erie The Behrend College United States The Hunan Provincial Key Lab of Bioinformatics Central South University Changsha 410083 China

ISBN: (纸本)157735897X

In this paper, we consider the k-center problem with outliers (the (k, z)-center problem) in the context of Massively Parallel Computation (MPC). Existing MPC algorithms for the (k, z)-center problem typically require Ω(k) local space per machine. While this may be feasible when k is small, these algorithms become impractical for large k, where each machine may lack sufficient space for computation. This motivates the study of fully-scalable algorithms with sublinear local space. We propose the first fully-scalable MPC algorithm for the (k, z)-center problem. The main challenge is to design an MPC algorithm that operates with sublinear local space for finding the inliers close to the optimal clustering centers, and ensuring the approximation loss remains bounded. To address this issue, we propose an iterative sampling-based algorithm with sublinear local space in the data size. A key component of our approach is an outliers-removal algorithm that adjusts the sample size in each iteration to select inliers as clustering centers. However, the number of discarded inliers increases with the iteration of the outliers-removal algorithm, making it difficult to bound. To address this, we propose a self-adaptive method that can automatically adjust sample size to account for different data distributions on each machine, ensuring a lower bound on the sampling success probability. With these techniques, we present an O(log^∗ n)approximation MPC algorithm for the (k, z)-center problem in constant-dimensional Euclidean space. The algorithm discards at most (1 + ϵ)z outliers, completing in O(log log n) computation rounds while using Θ(n^δ) local space per machine. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Clustering algorithms

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Norm Augmented Graph AutoEncoders for Link Prediction

Norm Augmented Graph AutoEncoders for Link Prediction

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

作者： Yunhui Liu Huaisong Zhang Xinyi Gao Liuye Guo Zhen Tao Tieke He State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Link Prediction (LP) is a crucial problem in graph-structured data. Graph Neural Networks (GNNs) have gained prominence in LP, with Graph AutoEncoders (GAEs) being a notable representation. However, our empirical findings reveal that GAEs’ LP performance suffers heavily from the long-tailed node degree distribution, i.e., low-degree nodes tend to exhibit inferior LP performance compared to high-degree nodes. What causes this degree-related bias, and how can it be mitigated? In this study, we demonstrate that the norm of node embeddings learned by GAEs exhibits variation among nodes with different degrees, underscoring its central significance in influencing the final performance of LP. Specifically, embeddings with larger norms tend to guide the decoder towards predicting higher scores for positive links and lower scores for negative links, thereby contributing to superior performance. This observation motivates us to improve GAEs’ LP performance on low-degree nodes by increasing their embedding norms, which can be implemented simply yet effectively by introducing additional self-loops into the training objective for low-degree nodes. This norm augmentation strategy can be seamlessly integrated into existing GAE methods with light computational cost. Extensive experiments on various datasets and GAE methods show the superior performance of norm-augmented GAEs.

关键词： Training Bridges Heavily-tailed distribution Autoencoders Signal processing Graph neural networks Acoustics Decoding Computational efficiency Speech processing

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PhantomPatch: Easy-ignoring Attacks on Object Detectors Using Ghosting Artifacts

PhantomPatch: Easy-ignoring Attacks on Object Detectors Usin...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Xiaolong Guo Qingsong Yao Jingwei Li Xuewen Dong Jianfeng Ma School of Cyber Engineering Xidian University Xi’an China State Key Lab of ISN School of Telecommunications Engineering Xidian University Xi’an China School of Computer Science and Technology Xidian University Xi’an China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Current patches used to attack object detectors are easily noticeable as abnormal. To mitigate this shortcoming, we devise an innovative technique named PhantomPatch, which leverages lens flare phenomena to attack object detectors, particularly in autonomous driving systems. Leveraging transfer-based adversarial examples, this method fools object detectors by projecting deceptive lens flares or ghost images, which are meaningless to people, while the light source looks like nearby light for people. Thus, it is easy to *** this way, we enable a cost-effective approach to manipulate the perception of the vehicle remotely. This strategy harmonizes adversarial patches with projecting image integrity correcting. Firstly, we propose to train a black-box transfer-based adversarial patch to fool the object-detecting system behind the camera. Then, the patch is printed and attached in front of a flashlight, which casts the patch onto the camera, resulting in a ghost image. We maintain the integrity of the image captured by the camera while casting the patch with image loss correction and optical distortion *** experimental results validate the effectiveness of PhantomPatch in evading existing object detectors such as YOLO V3/V5 and Faster R-CNN. Notably, during nocturnal scenarios, the technique achieves a success rate of 98.2%. Furthermore, our approach addresses limitations of existing methods, like conspicuousness and positional constraints, offering a low-cost and effective technique for adversarial attacks, especially for autonomous vehicles. Code and demo are available at https://***/rufus0803/PhantomPatch.

关键词： YOLO Optical losses Target recognition Closed box Detectors Optical distortion Cameras Robustness Autonomous vehicles Lenses

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