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International Conference on computer Supported Cooperative Work in Design

作者： Xiaowei Liu Wenhui Chen Mengmeng Sun Yali Si Zhen Chen School of Information Science and Engineering Yanshan University Qinhuangdao China School of Computer Science and Engineering Changshu Institute of Technology Changshu China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

With the popularity of the service-oriented development paradigm in the cloud era, numerous cloud APIs have been developed. The continuous proliferation of cloud APIs has submerged developers in a sea of options, bringing the need for cloud API recommendation techniques. Most existing cloud API recommendation methods focus on theoretical studies and neglect practical applications. As a result, these methods face several challenges, such as covering recommendation scenarios, generating real-time responses, and handling concurrent requests. In this paper, we propose and implement a practical cloud API complementary recommendation service for mashup creation. To address the above challenges, we first propose a cloud API complementary recommendation framework named FICR, which exploits feature interactions to mine implicit complementarity and models complementarity from the mashup side and the target cloud API side for scenario coverage. Then, we conduct performance optimization via precoding and batch size tuning to optimize FICR for real-time responses. Finally, we design a minimum-connection load balancing algorithm with resource early warning and build a distributed system for concurrent requests. Experiments conducted on real-world datasets show that FICR outperforms state-of-the-art baseline models in complementary recommendation scenarios, and demonstrate that the implemented service meets the real-time and concurrency requirements. The service will serve as a practical tool to help developers during mashup creation.

关键词： Concurrent computing Federated learning Mashups Precoding Load management Real-time systems Tuning

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Geo-Enhanced High-Order Feature Interaction Network for Cloud API QoS Prediction 19th

Geo-Enhanced High-Order Feature Interaction Network for Clou...

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19th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024

作者： Ma, Xinhong Zeng, Yehong Qi, Mingyang Si, Yali Chen, Zhen School of Information Science and Engineering Yanshan University Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao066004 China Changshu Institute of Technology Jiangsu Changshu215500 China

ISBN: (纸本)9789819623754

In the era of open cloud, cloud API is an important component and key enabling technology to achieve efficient data transmission, artificial intelligence algorithm empowerment, and software development cost reduction and efficiency. With the proliferation of cloud API with similar functionality, quality of service (QoS) becomes essential for differentiating cloud API performance, and QoS-driven prediction for cloud API has emerged as a critical topic for high quality cloud API selection. However, existing methods often inadequately utilize contextual information in resource-constrained scenario, limiting the improvement of cloud API prediction performance. We in this paper propose a geo-enhanced high-order feature interaction network (GHFIN) for cloud API QoS prediction. Firstly, a correlation analysis was conducted on real-world QoS dataset to explore the relationship between QoS data and geospatial context. Then, we design a geo-enhanced strategy that effectively utilizes the hierarchical relationships of geospatial entities (country, province, city, and district) through an offline real-time coordinate conversion module. On this basis, a geo-enhanced cloud API QoS prediction network GHFIN was designed, which aims to learn the high-order feature interactions through layer-wise feature stacking. Experimental results on two real-world QoS datasets illustrate the effectiveness of GHFIN compared to the classic and state-of-the-art methods, and our extensive analysis shows how the geospatial context and high-order feature interaction positively impact the performance of cloud API QoS prediction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Quality of service

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Improving Vision Transformers with Nested Multi-head Attentions

Improving Vision Transformers with Nested Multi-head Attenti...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Jiquan Peng Chaozhuo Li Yi Zhao Yuting Lin Xiaohan Fang Jibing Gong School of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Microsoft Research Asia Beijing China

Vision transformers have significantly advanced the field of computer vision in recent years. The cornerstone of these transformers is the multi-head attention mechanism, which models interactions between visual elements within a feature map. However, the vanilla multi-head attention paradigm independently learns parameters for each head, which ignores crucial interactions across different attention heads and may result in redundancy and under-utilization of the model’s capacity. To enhance model expressiveness, we propose a novel nested attention mechanism, Ne-Att, that explicitly models cross-head interactions via a hierarchical variational distribution. We conducted extensive experiments on image classification, and the results demonstrate the superiority of Ne-Att.

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Reinforced Heterogeneous Graphlet Design for Knowledge Graph Representation Learning

SSRN

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SSRN 2023年

作者： Gong, Jibing Lin, Yuting Zhao, Yi Lin, Tianyu Fang, Xiaohan Feng, Xinchao Peng, Jiquan School of Information Science and Engineering Yanshan University Qinhuangdao066004 China The Key Laboratory for Computer Virtual Technology System Integration of Hebei Province Yanshan University Qinhuangdao066004 China

Knowledge Graphs have been practical tools to represent and integrate plentiful structural and semantic information in mainstream industrial scenarios. While promising, the heterogeneity and complexity of KGs pose a formidable obstacle, especially for graph representation learning. Most existing KG embedding models omit the dynamic high-order connectivity patterns, and heavily rely on the handcrafted patterns to handle the complex semantic relationships, thereby limiting their ability to adaptively capture nuanced and intricate relationships of knowledge graphs in different tasks. To fill this gap, we present ReHGD — a model designed for KGs that focuses on adaptively designing typed graphlets through a cooperative Multi-agent Reinforcement Learning algorithm. This approach can learn more discriminative graph representations tailored to specific downstream tasks. Specifically, ReHGD engages in the creation of typed graphlets through a two-stage process: it (1) establishes a reinforced chain design module to generate chains without predefined rules, and (2) employs a buffer-aware sampling technique to derive episodic chains from prior experiences. Subsequently, motifs are deduced by applying commute count and Hadamard product operations to the episodic chain-based subgraphs. Finally, ReHGD utilize chain and motif aggregations to learn graph representations. Experimental results on three real-world KGs show that ReHGD outperforms baselines on entity classification. © 2023, The Authors. All rights reserved.

关键词： Knowledge graph

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Learning to Cluster Person in the Video via Graph Convolution Network

Learning to Cluster Person in the Video via Graph Convolutio...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Wei Li Sixin Liang Ang Bian Guodong Du College of Computer Science Sichuan University Chengdu China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province School of Information Science and Engineering Yanshan University Qinhuangdao P.R. China

Video person re-identification is receiving academic interest. However, the practical application of the algorithm is hardly supported because of prohibitive annotated data. Hence, the study for unlabeled data will lead to an attractive alternative. This work explores an innovative strategy, namely, learning to cluster unlabeled person in the video through graph convolutional networks. In this paper, we find that the possibility of inter-frame linkage can be inferred from context. Therefore, a pose-guided topology linkage clustering framework is proposed. Our framework consists of three modules: (i) a pose-guided representation module; (ii) a pose-guided embedding module; (iii) a link prediction module. Firstly, the representation coding alone is performed at the level of relational induction bias, embedding the implicit pose structure information in image features. Then, based on the consideration of the topology relationship between adjacent and cross-frame, graph convolutional network is introduced to infer the likelihood of linkage between frame nodes. Experiments show that the method demonstrates excellent scalability in addition to being an effective response to person clustering in case of changes, and does not need the number of clusters as a prior.

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Few-Shot Open-Set Object Detection via Conditional Evidence Decoupling

arXiv

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arXiv 2024年

作者： Wu, Zhaowei Su, Binyi Zhang, Hua Zhou, Zhong The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China The School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin300401 China The State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Zhongguancun Laboratory Beijing100190 China

Few-shot Open-set Object Detection (FOOD) poses a significant challenge in real-world scenarios. It aims to train an open-set detector under the condition of scarce training samples, which can detect known objects while rejecting unknowns. Under this challenging scenario, the decision boundaries of unknowns are difficult to learn and often ambiguous. To mitigate this issue, we develop a two-stage open-set object detection framework with prompt learning, which delves into conditional evidence decoupling for the unknown rejection. Specifically, we propose an Attribution-Gradient-based Pseudo-unknown Mining (AGPM) method to select region proposals with high uncertainty, which leverages the discrepancy in attribution gradients between known and unknown classes, alleviating the inadequate unknown distribution coverage of training data. Subsequently, we decouple known and unknown properties in pseudo-unknown samples to learn distinct knowledge with proposed Conditional Evidence Decoupling (CED), which enhances separability between knowns and unknowns. Additionally, we adjust the output probability distribution through Abnormal Distribution Calibration (ADC), which serves as a regularization term to establish robust decision boundaries for the unknown rejection. Our method has achieved superior performance over previous state-of-the-art approaches, improving the mean recall of unknown class by 7.24% across all shots in VOC10-5-5 dataset settings and 1.38% in VOC-COCO dataset settings. Copyright © 2024, The Authors. All rights reserved.

关键词： Zero-shot learning

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Optimal Subspace Analysis Based on Information-Entropy Increment 1

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8th China computer Federation International Conference on Big Data, CCF BIGDATA 2020

作者： Zhang, Zhongping Liu, Iiaojiao Zhang, Yuting Zhang, Jiyao Tian, Mingru College of Information Science and Engineering Yanshan University Qinhuangdao066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao066004 China Hebei Education Examinations Authority Shijiazhuang050000 China

ISBN: (数字)9789811607059

ISBN: (纸本)9789811607042

The data structure is becoming more and more complex, and the scale of the data set is getting larger and larger. The strong limitations and instability in the high-dimensional data environment is showed in traditional outlier detection method. To solve the problems, an Optimal subspace Analysis based on Information-entropy Increment is proposed. The concepts such as mutual information and dimensional entropy to re-portrait the indicators that measure the pros and cons of subspace clustering, optimize the objective function of the clustering subspace, and obtain the optimal subspace. According to the idea of dividing the information entropy increment by one, the entropy outlier score is proposed as a metric to detect outliers in the optimal subspace. Finally, experiments verify the effectiveness of the algorithm. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Clustering algorithms

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BAT: Mining Binary-API Topic for Multi-service Application Development

BAT: Mining Binary-API Topic for Multi-service Application D...

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International Conference on computer Supported Cooperative Work in Design

作者： Pengfei He Linlin Liu Dianlong You Limin Shen Zhen Chen School of Information Science and Engineering Yanshan University Qinhuangdao China National Science Libraries Chinese Academy of Sciences Beijing China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

With the growing popularity of API-driven multiservice application (mashup) development, the burgeoning web APIs have left developers drowning in the sea of web API selections. Matching developers with the most appropriate APIs is the key to improving user satisfaction and promoting more popular web applications. As a result, more and more researchers pay attention to web API recommender systems based on collaborative filtering. However, employing collaborative filtering to recommend APIs is challenging due to the severe sparsity of mashup and API interactions. To address this problem, we propose a probabilistic generative model, called the Binary-API Topic model (BAT), to parameterize mashups and APIs. Technically, BAT is equipped with a mechanism to extract binary-APIs and predict unknown pairwise interactions. To improve generality and capture more relevance from a limited number of interactions, we learn binary-API topics by directly modeling the generation of API co-occurrence patterns across the repository (all mashup collections from ***). The main advantage of BAT is that it preserves API co-occurrence patterns in model learning and exploits the rich global relevance. Finally, through extensive experiments, we demonstrate that BAT can achieve the highest performance on the sparse real-world data set.

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Transferable targeted adversarial attack via multi-source perturbation generation and integration

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Journal of Visual Communication and Image Representation 2025年 111卷

作者： Zhang, Shihui Han, Shaojie Yang, Sheng Han, Xueqiang Su, Junbin Zhai, Gangzheng Wang, Houlin School of Information Science and Engineering Yanshan University Hebei Qinhuangdao China Hebei Key Laboratory of Computer Virtual Technology and System Integration Hebei Qinhuangdao China

With the rapid development of artificial intelligence, deep learning models have been applied in the field of society (e.g., video or image representation). However, due to the presence of adversarial examples, these models exhibit obvious fragility, which has become a major challenge restricting society development. Therefore, studying the generation process and achieving high transferability of adversarial examples are of utmost importance. In this paper, we propose a transferable targeted adversarial attack method called Multi-source Perturbation Generation and Integration (MPGI) to address the vulnerability and uncertainty of deep learning models. Specifically, MPGI consists of three critical designs to achieve targeted transferability of adversarial examples. Firstly, we propose a Collaborative Feature Fusion (CFF) component, which reduces the impact of original example feature on model classification by considering collaboration in feature fusion. Subsequently, we propose a Multi-scale Perturbation Dynamic Fusion (MPDF) module to fuse perturbations from different scales for enriching perturbation diversity. Finally, we innovatively investigate a novel Logit Margin with Penalty (LMP) loss to further enhance the misleading ability of the examples. The LMP, as a pluggable part, offers the potential to be leveraged by different approaches for boosting performance. In summary, MPGI can effectively achieve targeted attacks, expose the shortcomings of existing models, and promote the development of artificial intelligence in terms of security. Extensive experiments on ImageNet-Compatible and CIFAR-10 datasets demonstrate the superiority of the proposed method. For instance, the attack success rate increases by 17.6% and 17.0% compared to state-of-the-art method when transferred from DN-121 to Inc-v3 and MB-v2 models. © 2025 Elsevier Inc.

关键词： Adversarial attacks Feature fusion Logit margin Perturbation generation Targeted transferability

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Mining Important Functions in Software Network by Node Vulnerability 2

Mining Important Functions in Software Network by Node Vulne...

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2019 2nd International Conference on computer Information Science and Artificial Intelligence, CISAI 2019

作者： Zhang, Bing Sun, Shengting Hao, Xiaobing School of Information Science and Engineering Yanshan University Hebei Qinhuangdao China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao City066004 China

Given that analysis on the vulnerability of functions is helpful to the detection and improvement of software security, this paper aims to propose an efficient methods to identify the vulnerable nodes (ITVN) in different software by the interdependence of functions. First, the dynamic software execution process was constructed as Software Execution Dependency Network (SEDN) based on Complex network theory. Second, by analyzing the dependency relationship among functions, the algorithm calVulAndScoOfNodes (CVSN) was designed to compute the vulnerability and the affected scope of each node for further analysis. Third, in order to measure the functions vulnerability in the whole software network, the algorithm calVulDegreeOfNodes (CVDN) was put forward to calculate the vulnerable degree of each node. Finally, the Vulnerable Nodes in different software were obtained by ITVN. Experimental results show that the vulnerable nodes selected as important nodes are well-reasoned in software network by testing different software, and the measures are effective for evaluating nodes vulnerability. © Published under licence by IOP Publishing Ltd.

关键词： Software testing

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