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International Joint Conference on Neural Networks (IJCNN)

作者： Xiancong Chen Weike Pan Zhong Ming National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen University College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

In this paper, we study a recent and important recommendation problem called heterogeneous one-class collaborative filtering (HOCCF), where we have two different types of one-class feedback, i.e., a set of browses and a set of purchases, for preference learning. Previous methods exploit the browses and purchases by extending some existing methods on modeling homogeneous one-class feedback in an integrative or sequential manner. However, an integrative method may be of high complexity in model training due to the expanded prediction rule, and a sequential method is of inconvenience in deployment because more than one parametric models have to be maintained. In this paper, we convert the HOCCF problem to an adaptive transfer learning task, where we first model browses via a factorization model from a perspective of the role of browser, and then adaptively refine the learned model parameters via purchases from a perspective of the dependent role of purchaser. Based on this conversion, we design a novel solution called role-based adaptive factorization (ROAF), and then derive two specific variants with pairwise preference learning and pointwise preference learning. Finally, we conduct extensive empirical studies on two large datasets, and find that our ROAF is a very promising solution in terms of recommendation accuracy, besides its convenience of one single parametric model in deployment.

关键词： Browsers Adaptation models Parametric statistics Training Bayes methods Task analysis

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A data-Centric Accelerator for High-Performance Hypergraph Processing 22

A Data-Centric Accelerator for High-Performance Hypergraph P...

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Proceedings of the 55th Annual IEEE/ACM International Symposium on Microarchitecture

作者： Qinggang Wang Long Zheng Ao Hu Yu Huang Pengcheng Yao Chuangyi Gui Xiaofei Liao Hai Jin Jingling Xue National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China and Zhejiang Lab Hangzhou China National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Computer Science and Engineering University of New South Wales Sydney NSW Australia

ISBN: (纸本)9781665462723

Hypergraph processing has emerged as a powerful approach for analyzing complex multilateral relationships among multiple entities. Past research on building hypergraph systems suggests that changing the scheduling order of bipartite edge tasks can improve the overlap-induced data locality in hypergraph processing. However, due to the complex intertwined connections between vertices and hyperedges, it is almost impossible to find a locality-optimal scheduling order. Thus, these task-centric hypergraph systems often suffer from substantial off-chip *** this paper, we first propose a novel data-centric Load-Trigger-Reduce (LTR) execution model to exploit fully the locality in hypergraph processing. Unlike a task-centric model that loads the required data along with a task, our LTR model invokes tasks as per the data used. Specifically, once the hypergraph data is loaded into the on-chip memory, all of its relevant computation tasks will be triggered simultaneously to output intermediate results, which are finally reduced to update the final results. Our LTR model enables all hypergraph data to be accessed once in each iteration. To fully exploit the LTR performance potential, we further architect an LTR-driven hypergraph accelerator, XuLin, which features with an adaptive data loading mechanism to minimize the loading cost via chunk merging at runtime. XuLin is also equipped with a priority-based differential data reduction scheme to reduce the impact of conflicting updates on performance. We have implemented XuLin both on a Xilinx Alveo U250 FPGA card and using a cycle-accurate simulator. The results show that XuLin outperforms the state-of-the-art hypergraph processing solutions Hygra and ChGraph by 20.47× and 8.77× on average, respectively.

关键词： hypergraph data-centric accelerator

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Multi-task representation learning with multi-view graph convolutional networks

arXiv

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arXiv 2021年

作者： Huang, Hong Song, Yu Wu, Yao Shi, Jia Xie, Xia Jin, Hai National Engineering Research Center for Big Data Technology Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Link prediction and node classification are two important downstream tasks of network representation learning. Existing methods have achieved acceptable results but they perform these two tasks separately, which requires a lot of duplication of work and ignores the correlations between tasks. Besides, conventional models suffer from the identical treatment of information of multiple views, thus they fail to learn robust representation for downstream tasks. To this end, we tackle link prediction and node classification problems simultaneously via multi-task multi-view learning in this paper. We first explain the feasibility and advantages of multi-task multi-view learning for these two tasks. Then we propose a novel model named as MT-MVGCN to perform link prediction and node classification tasks simultaneously. More specifically, we design a multi-view graph convolutional network to extract abundant information of multiple views in a network, which is shared by different tasks. We further apply two attention mechanisms: view attention mechanism and task attention mechanism to make views and tasks adjust the view fusion process. Moreover, view reconstruction can be introduced as an auxiliary task to boost the performance of the proposed model. Experiments on real-world network datasets demonstrate that our model is efficient yet effective, and outperforms advanced baselines in these two tasks. © 2021, CC BY.

关键词： data mining

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PAT: Preference-Aware Transfer Learning for Recommendation with Heterogeneous Feedback

PAT: Preference-Aware Transfer Learning for Recommendation w...

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International Joint Conference on Neural Networks (IJCNN)

作者： Feng Liang Wei Dai Yunfeng Huang Weike Pan Zhong Ming National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen University College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

In this paper, we study an important recommendation problem with heterogeneous feedback of users' grade scores such as 5-star grade scores and like/dislike binary ratings assigned to items. As a response, we address the problem from a transfer learning perspective, i.e., taking the grade scores as the target data and the binary ratings as the auxiliary data, in order to share knowledge between two different types of data more sufficiently. Technically, besides the observed explicit feedback of grade scores and binary ratings, we propose to exploit the implicit preference context beneath the feedback, which is incorporated into the prediction process of users' grade scores to items. Finally, we develop a novel and generic transfer learning solution, i.e., preference-aware transfer (PAT), which embodies several recent algorithms as parts of our solution and special cases. To verify our novel solution, we then conduct extensive empirical studies on two large and public datasets and find that our PAT performs significantly better than the state-of-the-art methods.

关键词： Collaboration Matrix decomposition Predictive models data models Probabilistic logic Training Context modeling

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International Conference on big data and Smart computing (bigCOMP)

ISBN: (数字)9781728160344

ISBN: (纸本)9781728160351

In this paper, we focus on a recently studied problem in recommendation called heterogeneous one-class collaborative filtering (HOCCF), which consists of different types of feedback, e.g., target feedback and auxiliary feedback. The main challenge of HOCCF is how to make good use of the large-scale and ambiguous auxiliary feedback to assist the task of learning users'true preferences. Inspired by a recent method that jointly learns the similarity among items associated with the target feedback and auxiliary feedback in a transfer learning manner, we further extend this method by learning additional similarity among users for better modeling users' preferences. Specifically, we jointly learn the item-based similarity and user-based similarity by leveraging the target feedback and auxiliary feedback, and then aggregate the dual similarity into one single prediction rule for estimating users' preferences, which is thus termed as dual similarity learning model (DSLM). Extensive empirical studies on three public datasets show that our DSLM with dual similarity is more effective in terms of recommendation accuracy, demonstrating that our method strikes a good balance between the item-based similarity and user-based similarity.

关键词： Collaboration IP networks Aggregates big data Predictive models Indexes Uncertainty

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Towards Demystifying Android Adware: dataset and Payload Location 39

Towards Demystifying Android Adware: Dataset and Payload Loc...

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39th ACM/IEEE International Conference on Automated Software engineering Workshops, ASEW 2024

作者： Wang, Chao Liu, Tianming Zhao, Yanjie Zhang, Lin Du, Xiaoning Li, Li Wang, Haoyu Monash University Clayton Australia Huazhong University of Science and Technology Wuhan China School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security China The National Computer Emergency Response Beijing China Beihang University Beijing China

ISBN: (纸本)9798400712494

Adware represents a pervasive threat in the mobile ecosystem, yet its inherent characteristics have been largely overlooked by previous research. This work takes a crucial step towards demystifying Android adware. We present a comprehensive, well-annotated Android adware dataset AdwareZoo, which comprises 15,996 adware samples across 118 distinct adware families collected from both security reports and app repositories, providing a robust foundation for in-depth analysis and future research. We identify adware family payloads by isolating packages from adware samples for VirusTotal rescanning. Our analysis unveils critical insights into the adware ecosystem, highlighting distinctive patterns in family naming conventions and exposing the unexpected classification of widely-used ad networks as adware. We identify diverse payload location strategies, with a notable finding that over 30% of adware families employ payloads beyond conventional Java/Kotlin code. Furthermore, we reveal several evasion techniques utilized by ad-ware, including package name obfuscation and dynamic payload loading. This research not only offers a robust foundation for understanding and combating adware but also highlights the pressing need for increased scrutiny of mobile advertising practices. © 2024 ACM.

关键词： Android malware

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Sequence-Aware Factored Mixed Similarity Model for Next-Item Recommendation

Sequence-Aware Factored Mixed Similarity Model for Next-Item...

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International Conference on big data and Smart computing (bigCOMP)

作者： Liulan Zhong Jing Lin Weike Pan Zhong Ming National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen University College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9781728160344

ISBN: (纸本)9781728160351

Sequence-aware next-item recommendation has recently been studied because of the noteworthy usefulness of the sequential information integrated into recommendation algorithms. Following the development thread of sequential recommendation methods, especially the factored Markov chains segment, we propose a novel sequence-aware factored mixed similarity model (S-FMSM) based on a well-designed fusing similarity model with factored high-order Markov chains (i.e., Fossil). Unlike the baseline methods that model the general representation and the sequential representation in two divided factorization components, we use a factored mixed similarity model that unites the general similarity and the sequential relationship between two successive items for their sequential representation learning. Experiments on six datasets show that our newly introduced general similarity can notably improve the results of the recommended ranking lists. And a study on tuning the prior tradeoff parameter indicates the importance of the general similarity on different datasets.

关键词： Markov processes Linear programming Probability big data Collaboration Feeds Conferences

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GIDN: A Lightweight Graph Inception Diffusion Network for High-efficient Link Prediction

arXiv

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arXiv 2022年

作者： Wang, Zixiao Guo, Yuluo Zhao, Jin Zhang, Yu Yu, Hui Liao, Xiaofei Wang, Biao Yu, Ting National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Zhejiang Lab Hangzhou China

In this paper, we propose a Graph Inception Diffusion Networks(GIDN) model. This model generalizes graph diffusion in different feature spaces, and uses the inception module to avoid the large amount of computations caused by complex network structures. We evaluate GIDN model on Open Graph Benchmark(OGB) datasets, reached an 11% higher performance than AGDN on ogbl-collab dataset. Copyright © 2022, The Authors. All rights reserved.

关键词： Complex networks

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PointFaceFormer: Local and Global Attention Based Transformer for 3D Point Cloud Face Recognition

PointFaceFormer: Local and Global Attention Based Transforme...

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International Conference on Automatic Face and Gesture Recognition

作者： Ziqi Gao Qiufu Li Gui Wang Linlin Shen Computer Vision Institute Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer Science University of Nottingham Ningbo China

ISBN: (数字)9798350394948

ISBN: (纸本)9798350394955

Existing 3D point cloud-based facial recognition struggles to fully leverage both global and local information inherent in the 3D point cloud data. In this paper, we introduce the PointFaceFormer, the first Transformer model designed for 3D point cloud face recognition. It incorporates an attention mechanism based on dot product and cosine functions to construct a similarity Transformer architecture, which effectively extracts both local and global features from the point cloud data. Experimental results demonstrate that PointFaceFormer achieves a recognition accuracy of 89.08% and a verification accuracy of 76.93% on the large-scale facial point cloud dataset Lock3DFace, which is a new state-of-the-art in 3D face recognition. Furthermore, PointFaceFormer exhibits excellent generalization performance on cross-quality datasets. Additionally, we validate the effectiveness of the attention mechanism through ablation experiments, which justify the effectiveness of the proposed modules.

关键词： Point cloud compression Solid modeling Three-dimensional displays Attention mechanisms Accuracy Face recognition Transformers

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An Efficient Block Validation Mechanism for UTXO-based Blockchains

An Efficient Block Validation Mechanism for UTXO-based Block...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Xiaohai Dai Bin Xiao Jiang Xiao Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing The Hong Kong Polytechnic University Hong Kong

It has been recognized that one of the bottlenecks in the UTXO-based blockchain systems is the slow block validation - the process of validating a newly-received block by a node before locally storing it and further broadcasting it. As a block contains multiple inputs, the block validation mainly involves checking the inputs against the status data, which is also known as the Unspent Transaction Outputs (UTXO) set. As time goes by, the UTXO set becomes more and more expansive, most of which can only be stored on disks. This considerably slows down the input checking and thus block validation, which can potentially compromise system security. To deal with the above problem, we disassemble the function of input checking into three parts: existence validation (EV), unspent validation (UV), and script validation (SV). Based on the disassembly, we propose EBV, an efficient block validation mechanism to speed up EV, UV, and SV individually. First, EBV changes the representation of status data, from UTXO set to a bit-vector set, which drastically reduces its size. The smaller status data can be entirely maintained in memory, thereby accelerating UV and also block validation. Second, EBV requires each transaction to carry the proof data, which enables EV and SV without accessing the disks. Furthermore, we also cope with two challenges in the design of EBV, namely transaction inflation and fake positions. To evaluate the EBV mechanism, we implement a prototype on top of Bitcoin, the most widely known UTXO-based blockchain, and conduct extensive experiments to compare EBV and Bitcoin. The experimental results demonstrate that EBV successfully reduces the memory requirement by 93.1 % and the block validation time by up to 93.5%.

关键词： Distributed processing Memory management Prototypes Bitcoin Broadcasting Blockchains Delays

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