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39th IEEE International Conference on data Engineering, ICDE 2023

作者： Zhai, Shuoyao Liu, Baichuan Yang, Deqing Xiao, Yanghua Fudan University School of Data Science Shanghai200433 China Fudan University School of Computer Science Shanghai200433 China Shanghai Key Laboratory of Data Science Shanghai200433 China

ISBN: (纸本)9798350322279

In recent years, group buying has become one popular kind of online shopping activities, thanks to its larger sales and lower unit price. Unfortunately, seldom research focuses on the recommendations specifically for group buying by now. Although some recommendation models have been proposed for group recommendation, they can not be directly used to achieve the real-world group buying recommendation, due to the essential difference between group recommendation and group buying recommendation. In this paper, we first formalize the task of group buying recommendation into two sub-tasks. Then, based on our insights into the correlations and interactions between the two sub-tasks, we propose a novel recommendation model for group buying, namely MGBR, which is built mainly with a multi-task learning module. To improve recommendation performance further, we devise some collaborative expert networks and adjusted gates in the multi-task learning module, to promote the information interaction between the two sub-tasks. Furthermore, we propose two auxiliary losses corresponding to the two sub-tasks, to refine the representation learning in our model. Our extensive experiments not only demonstrate that the augmented representations learned in our model result in better performance than previous recommendation models, but also justify the impacts of the specially designed components in our model. To reproduce our model's recommendation results conveniently, we have provided our model's source code and dataset on https://***/DeqingYang/MGBR. © 2023 IEEE.

关键词： Learning systems

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A Background Knowledge Revising and Incorporating Dialogue Model

A Background Knowledge Revising and Incorporating Dialogue M...

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作者： Zhao, Xinyan Feng, Xiao Chen, Huanhuan University of Science and Technology of China School of Data Science Hefei230027 China University of Science and Technology of China School of Computer Science and Technology Hefei230027 China

Currently, dialogue systems have attracted increasing research interest. In particular, background knowledge is incorporated to improve the performance of dialogue systems. Existing dialogue systems mostly assume that the background knowledge is correct and comprehensive. However, low-quality background knowledge is common in real-world applications. On the other hand, dialogue datasets with manual labeled background knowledge are often insufficient. To tackle these challenges, this article presents an algorithm to revise low-quality background knowledge, named background knowledge revising transformer (BKR-Transformer). By innovatively formulating the knowledge revising task as a sequence-to-sequence (Seq2Seq) problem, BKR-Transformer generates the revised background knowledge based on the original background knowledge and dialogue history. More importantly, to alleviate the effect of insufficient training data, BKR-Transformer introduces the ideas of parameter sharing and tensor decomposition, which could significantly reduce the number of model parameters. Furthermore, this work presents a background knowledge revising and incorporating dialogue model that combines the background knowledge revision with response selection in a unified model. Empirical analyses on real-world applications demonstrate that the proposed background knowledge revising and incorporating dialogue system (BKRI) could revise most low-quality background knowledge and substantially outperforms previous dialogue models. © 2012 IEEE.

关键词： Tensors

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Teaching Evaluation Index of College Students Based on Random Forest 3

Teaching Evaluation Index of College Students Based on Rando...

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3rd International Conference on Educational Technology, ICET 2023

作者： Jiang, Mingchi Huang, Xiao Liu, Dandan Hu, Shengbo School of Big Data and Computer Science Guizhou Normal University Guiyang China

ISBN: (纸本)9798350302332

In order to solve the problem of unscientific setting of evaluation indicators due to the non-standardized evaluation scale of college students, we propose to use random forest algorithm to model and analyze the evaluation indicators of students. Taking the course "programming Fundamentals"as an example, the teaching evaluation questionnaire is designed, and the random forest algorithm is used to model, compare the measured value and predicted value of the comprehensive scores, and obtain the importance ranking of the teaching evaluation indicators. The experimental results show that teaching effect, Language expression and other teaching evaluation indicators occupy an important position in the scale of university students' evaluation, and have important reference value for the design of the scale. © 2023 IEEE.

关键词： Students

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Optimizing Coverage in Wireless Sensor Networks Using the Tumbleweed Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第1期10卷 250-261页

作者： Zhang, Mengfei Chu, Shu-Chuan Liu, Nengxian Huang, Kuan-Chun Pan, Jeng-Shyang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Department of Industrial and Systems Engineering Chung Yuan Christian University Taoyuan320314 Taiwan School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing210044 China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan

Wireless sensor networks (WSNs) have found extensive applications across various fields, significantly enhancing the convenience in our daily lives. Hence, an in-creasing number of researchers are directing their attention toward the performance of sensor nodes, with a particular emphasis on addressing the coverage challenges in WSNs. Node coverage plays a crucial role in determining the detection performance of WSNs, making it imperative to attain maximum coverage with a given number of sensor nodes. This paper applies the Tumbleweed Algorithm (TA) to the coverage problem in WSNs and validates its effectiveness through simulation experiments. The results demonstrate its unique advantages in global search and avoiding local optima, providing a new solution for the WSN coverage problem and offering valuable references for future research and practical applications. The TA algorithm simulates the lifecycle of tumbleweed from seedling to mature plant and includes the concept of growth cycles. This paper compares the TA algorithm with PSO, GWO, DE, BOA, SCA, and AOA, setting different numbers of wireless sensor nodes and varying sensing radii for the experiments. The experimental results show that the TA algorithm significantly improves coverage rates under various configurations and consistently outperforms other commonly used algorithms. © 2025, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Sensor nodes

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Analysis of MFCC and Chromagram Features in Lightweight Bidirectional LSTM and Attention Models for Music Genre Classification 21

Analysis of MFCC and Chromagram Features in Lightweight Bidi...

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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Huang, Renxiang Li, Xu Li, Jianping School of Artificial Intelligence and Big Data Sichuan University of Arts and Science China Khoury College of Computer Science Northeastern university BostonMA02115 United States School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China

ISBN: (纸本)9798331519254

Music genre classification is essential for organizing music libraries and enhancing recommendation systems. This paper evaluates four lightweight models combining Mel Frequency Cepstral Coefficients (MFCCs) and Chromagram features with Bidirectional LSTM and Multi-head Attention architectures. Using the GTZAN dataset, we found that models with Multi-head Attention, outperformed with MFCCs features, achieving a test accuracy of 85.6%. MFCCs excel in capturing timbral characteristics critical for genre classification, while Chromagram features proved less effective. Although overfitting occurred, future studies can focus on incorporating methods like data augmentation and regularization to improve performance. These lightweight models offer a balance between accuracy and computational efficiency, making them suitable for real-time applications. © 2024 IEEE.

关键词： Real time systems

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Eight Points Geometric Constraint for Stereo Matching 9

Eight Points Geometric Constraint for Stereo Matching

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9th International Conference on Systems and Informatics, ICSAI 2023

作者： Wang, Yuanbin Xiamen Institute of Technology School of Data and Computer Science Xiamen361021 China

ISBN: (纸本)9798350383706

Stereo point matching is a critical and fundamental problem in 3D vision. Numerous algorithms have been proposed to solve this problem. Since images obtained from 3D to 2D projection of a 3D scene lose depth information of the 3D scene, there is no numerical geometric invariant between stereo images. So, we do not have similar algorithms that use geometric invariants for point matching as in the case of 2D to 2D projective transformations. However, it is discovered that geometric constraints can be used to restrict the search space and eliminate faulty correspondences. In this paper we derive a new mathematical constraint relating eight corresponding points in stereo images. It is a simple formula consisting of coordinates of the eight points that compute to zero for the eight points to be in correspondence. This constraint can be used as a preliminary necessity testing step before other time-consuming methods to speed up the matching procedure. © 2023 IEEE.

关键词： Stereo image processing

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Prefetching-based Adaptive Video Streaming Strategy on Edge Computing 29

Prefetching-based Adaptive Video Streaming Strategy on Edge ...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Yi, Xuanyu Zhou, Jipeng Jinan University Department of Computer Science Guangzhou China Jinan University School of Data Science of Guangzhou Huashang College Department of Computer Science Guangzhou China

ISBN: (纸本)9798350330717

Dynamic Adaptive Streaming over HTTP (DASH) is a widely adopted video streaming protocol. Adaptive Bitrate Streaming (ABR) algorithm is utilized to dynamically switch between different bitrates. However, traditional ABR algorithms have gradually failed to meet users' demands for high-quality video transmission, especially in complex network environments. Thus, the current research focus has shifted towards enhancing the accuracy of algorithm. The advent of edge computing has brought new possibilities for DASH transmission. Edge computing-based algorithms can make decisions from a more macroscopic perspective, which can enhance algorithm efficiency. In this paper, we propose an edge computing-based strategy that enables the edge server to obtain the actual file size of the next segment at every bitrate. As a result, edge servers can obtain more informations. We further propose an edge-based system model that assists the ABR algorithm in achieving better operational efficiency. With additional information, the algorithm located at the edge node possesses the capability to make more precise decisions, which is advantageous for enhancing the quality of experience (QoE) for users. Our experiments demonstrate that the proposed strategy can significantly enhance QoE and network resource utilization. © 2023 IEEE.

关键词： DASH Edge Computing Video Streaming

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Soft-GNN:towards robust graph neural networks via self-adaptive data utilization

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Frontiers of computer science 2025年第4期19卷 1-12页

作者： Yao WU Hong HUANG Yu SONG Hai JIN National Engineering Research Center for Big Data Technology and System Service Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China College of Information and Communication National University of Defense TechnologyWuhan 430019China Department of Computer Science and Operations Research Universitéde MontréalMontreal H3C 3J7Canada

Graph neural networks(GNNs)have gained traction and have been applied to various graph-based data analysis tasks due to their high ***,a major concern is their robustness,particularly when faced with graph data that has been deliberately or accidentally polluted with *** presents a challenge in learning robust GNNs under noisy *** address this issue,we propose a novel framework called Soft-GNN,which mitigates the influence of label noise by adapting the data utilized in *** approach employs a dynamic data utilization strategy that estimates adaptive weights based on prediction deviation,local deviation,and global *** better utilizing significant training samples and reducing the impact of label noise through dynamic data selection,GNNs are trained to be more *** evaluate the performance,robustness,generality,and complexity of our model on five real-world datasets,and our experimental results demonstrate the superiority of our approach over existing methods.

关键词： graph neural networks node classification label noise robustness

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CBPF: A Novel Method For Filtering Poisoned data Based on Composite Backdoor Attacks

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IEEE Internet of Things Journal 2025年第13期12卷 25136-25147页

作者： Xia, Hanfeng Hong, Haibo Wang, Ruili Sun, Yiru Ding, Hao Zhejiang Gongshang University Zhejiang Key Laboratory of Big Data and Future E-Commerce Technology School of Computer Science and Technology Hangzhou China Massey University School of Mathematical and Computational Sciences Auckland New Zealand

Backdoor attacks involve the injection of a limited quantity of poisoned samples containing triggers into the training dataset. During the inference stage, backdoor attacks can uphold a high level of accuracy for normal examples, yet when presented with trigger-containing instances, the model may erroneously predict them as the targeted class designated by the attacker. This paper addresses the challenge of backdoor attacks by developing a novel method for filtering poisoned samples. We primarily leverage two key characteristics of backdoor attacks: 1) Multiple backdoors can exist simultaneously within a single model;2) The discovery through Composite Backdoor Attack (CBA) that altering two triggers in a sample to new target labels does not compromise the original functionality of the triggers, yet enables the prediction of the data as a new target class when both triggers are present simultaneously. Therefore, a novel three-stage poisoning data filtering approach, known as Composite Backdoor Poisoning Filtering (CBPF), is proposed as an effective solution. Firstly, utilizing the identified distinctions in output between poisoned and clean samples, a subset of data is partitioned to include both poisoned and clean data. Subsequently, benign triggers are incorporated and labels are adjusted to create new target and benign target classes, thereby prompting the poisoned and clean data to be classified as distinct entities during the inference stage. The experimental results indicate that CBPF is successful in filtering out poisoned data produced by seven advanced attacks on CIFAR-10, GTSRB and ImageNet-12. On average, CBPF attains a notable filtering success rate of 99.88% for these attacks on CIFAR-10. Additionally, the model trained on the uncontaminated samples exhibits sustained high accuracy levels. © 2014 IEEE.

关键词： Wiener filtering

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The Effect of Social Media Influencers on Business Sustainability: Understanding Customer Behavior Change Towards Sustainable Usage Patterns 6

The Effect of Social Media Influencers on Business Sustainab...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Herlina, Maria Grace Budiharto, Alexandra Li Lukito, Elizabeth Margaretha Iskandar, Karto Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

The study aims to fill existing research gaps by creating a new framework that explores the impact of social media influencers on sustainable usage patterns, focusing on business sustainability as a mediating factor. In this research, non-probability judgmental and purposive sampling methods were used. Due to the size and distribution of the population, sampling was also chosen based on the researcher's own judgment and experience. Structural equation modeling (SEM) is used as the data analysis method. Path coefficient analysis yielded significant findings. Social media influencers were found to influence business sustainability significantly. Apart from that, social media influencers have a significant negative affect on sustainable usage patterns. Business sustainability also significantly influences sustainability usage patterns, with a powerful influence. This enriches the dynamics of business sustainability, proposing new knowledge about how it influences this relationship. © 2024 IEEE.

关键词： Sustainable development goals

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