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897 results on '"Sheng, Victor S."'

152. Social Personalized Ranking Embedding for Next POI Recommendation

Author

Long, Yan, Zhao, Pengpeng, Sheng, Victor S., Liu, Guanfeng, Xu, Jiajie, Wu, Jian, Cui, Zhiming, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bouguettaya, Athman, editor, Gao, Yunjun, editor, Klimenko, Andrey, editor, Chen, Lu, editor, Zhang, Xiangliang, editor, Dzerzhinskiy, Fedor, editor, Jia, Weijia, editor, Klimenko, Stanislav V., editor, and Li, Qing, editor

Published
2017
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153. Social Recommendation Terms: Probabilistic Explanation Optimization

Author

Liu, Jie, Zhang, Lin, Sheng, Victor S., Laili, Yuanjun, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Zhang, Lin, editor, Ren, Lei, editor, and Kordon, Fabrice, editor

Published
2017
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163. A Double Weighted Naive Bayes for Multi-label Classification

Author

Yan, Xuesong, Li, Wei, Wu, Qinghua, Sheng, Victor S., Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Liu, Ting, Series editor, Kotenko, Igor, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Li, Kangshun, editor, Li, Jin, editor, Liu, Yong, editor, and Castiglione, Aniello, editor

Published
2016
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164. A Hybrid Machine-Crowdsourcing Approach for Web Table Matching and Cleaning

Author

Li, Chunhua, Zhao, Pengpeng, Sheng, Victor S., Li, Zhixu, Liu, Guanfeng, Wu, Jian, Cui, Zhiming, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Cui, Bin, editor, Zhang, Nan, editor, Xu, Jianliang, editor, Lian, Xiang, editor, and Liu, Dexi, editor

Published
2016
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165. An Efficient Location-Aware Top-k Subscription Matching for Publish/Subscribe with Boolean Expressions

Author

Jiang, Hanhan, Zhao, Pengpeng, Sheng, Victor S., Xu, Jiajie, Liu, An, Wu, Jian, Cui, Zhiming, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Navathe, Shamkant B., editor, Wu, Weili, editor, Shekhar, Shashi, editor, Du, Xiaoyong, editor, Wang, Sean X., editor, and Xiong, Hui, editor

Published
2016
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166. Semi-Supervised Local Community Detection

Author

Ni, Li, Ge, Junnan, Zhang, Yiwen, Luo, Wenjian, and Sheng, Victor S.

Abstract

Owing to the lack of a universal definition of communities, some semi-supervised community detection approaches learn the concept of community structures from known communities, and then dig out communities using learned concepts of communities. In some cases, users are only interested in the community containing a given node. However, communities detected by these semi-supervised approaches may not contain a given node. Besides, these methods traverse the entire network to detect many communities and cost more resources than a local algorithm. Therefore, it is necessary and meaningful to find the local community that contains a given node with prior information on the local network around the given node. We call this a Semi-supervised Local Community Detection (SLCD) problem. In this paper, prior information refers to certain known communities. To address the SLCD problem, we propose the Semi-supervised Local community detection with the Structural Similarity algorithm, called SLSS, which uses some known communities instead of all known communities. The idea of SLSS is to use the structural similarity between the known communities and the detected community, calculated by the graph kernel, to guide the expansion of the community. Experimental results show that SLSS outperforms other algorithms on six real-world datasets.

Published
2024
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169. RoseCliff Algorithm: Making Passwords Dynamic.

Author

Umejiaku, Afamefuna P. and Sheng, Victor S.

Subjects
ALGORITHMS, TECHNOLOGICAL innovations, CYBERTERRORISM, QUANTUM computers, COMPUTER passwords, USER experience
Abstract

Authentication in the digital landscape faces persistent challenges due to evolving cyber threats. Traditional text-based passwords, which are vulnerable to various attacks, necessitate innovative solutions to fortify user systems. This paper introduces the RoseCliff Algorithm, which is a dual authentication mechanism designed to enhance resilience against sophisticated hacking attempts and to continuously evolve stored passwords. The study explores encryption techniques, including symmetric, asymmetric, and hybrid encryption, thereby addressing the emerging threats posed by quantum computers. The RoseCliff Algorithm introduces introduces dynamism into passwords that allows for more secured communication across multiple platforms. To assess the algorithm's robustness, potential attacks such as brute force, dictionary attacks, man-in-the-middle attacks, and machine learning-based attacks are examined. The RoseCliff Algorithm, through its dynamic password generation and encryption methodology, proves effective against these threats. Usability evaluation encompasses the implementation and management phase, focusing on seamless integration, and the user experience, emphasizing clarity and satisfaction. Limitations are acknowledged, thus urging further research into encryption technique resilience, robustness against breaches, and the integration of emerging technologies. In conclusion, the RoseCliff Algorithm emerges as a promising solution, thereby effectively addressing the complexities of modern authentication challenges and providing a foundation for future research and enhancements in digital security. [ABSTRACT FROM AUTHOR]

Published
2024
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170. Gate-Attention and Dual-End Enhancement Mechanism for Multi-Label Text Classification.

Author

Jieren Cheng, Xiaolong Chen, Wenghang Xu, Shuai Hua, Zhu Tang, and Sheng, Victor S.

Subjects
SHORT-term memory, LONG-term memory, FEATURE extraction, CLASSIFICATION
Abstract

In the realm of Multi-Label Text Classification (MLTC), the dual challenges of extracting rich semantic features from text and discerning inter-label relationships have spurred innovative approaches. Many studies in semantic feature extraction have turned to external knowledge to augment the model's grasp of textual content, often overlooking intrinsic textual cues such as label statistical features. In contrast, these endogenous insights naturally align with the classification task. In our paper, to complement this focus on intrinsic knowledge, we introduce a novel Gate-Attention mechanism. This mechanism adeptly integrates statistical features from the text itself into the semantic fabric, enhancing the model's capacity to understand and represent the data. Additionally, to address the intricate task of mining label correlations, we propose a Dual-end enhancement mechanism. This mechanism effectively mitigates the challenges of information loss and erroneous transmission inherent in traditional long short term memory propagation. We conducted an extensive battery of experiments on the AAPD and RCV1-2 datasets. These experiments serve the dual purpose of confirming the efficacy of both the Gate-Attention mechanism and the Dual-end enhancement mechanism. Our final model unequivocally outperforms the baseline model, attesting to its robustness. These findings emphatically underscore the imperativeness of taking into account not just external knowledge but also the inherent intricacies of textual data when crafting potent MLTC models. [ABSTRACT FROM AUTHOR]

Published
2023
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176. Improving Label Accuracy by Filtering Low-Quality Workers in Crowdsourcing

Author

Nicholson, Bryce, Sheng, Victor S., Zhang, Jing, Wang, Zhiheng, Xian, Xuefeng, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Sidorov, Grigori, editor, and Galicia-Haro, Sofía N., editor

Published
2015
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177. Ranked Reverse Boolean Spatial Keyword Nearest Neighbors Search

Author

Fang, Hailin, Zhao, Pengpeng, Sheng, Victor S., Li, Zhixu, Xu, Jiajie, Wu, Jian, Cui, Zhiming, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Wang, Jianyong, editor, Cellary, Wojciech, editor, Wang, Dingding, editor, Wang, Hua, editor, Chen, Shu-Ching, editor, Li, Tao, editor, and Zhang, Yanchun, editor

Published
2015
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178. An Efficient Location-Aware Publish/Subscribe Index with Boolean Expressions

Author

Jiang, Hanhan, Zhao, Pengpeng, Sheng, Victor S., Liu, Guanfeng, Liu, An, Wu, Jian, Cui, Zhiming, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Wang, Jianyong, editor, Cellary, Wojciech, editor, Wang, Dingding, editor, Wang, Hua, editor, Chen, Shu-Ching, editor, Li, Tao, editor, and Zhang, Yanchun, editor

Published
2015
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179. EPEMS: An Entity Matching System for E-Commerce Products

Author

Gao, Lei, Zhao, Pengpeng, Sheng, Victor S., Li, Zhixu, Liu, An, Wu, Jian, Cui, Zhiming, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Cheng, Reynold, editor, Cui, Bin, editor, Zhang, Zhenjie, editor, Cai, Ruichu, editor, and Xu, Jia, editor

Published
2015
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180. Semi-automatic Labeling with Active Learning for Multi-label Image Classification

Author

Wu, Jian, Ye, Chen, Sheng, Victor S., Yao, Yufeng, Zhao, Pengpeng, Cui, Zhiming, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Ho, Yo-Sung, editor, Sang, Jitao, editor, Ro, Yong Man, editor, Kim, Junmo, editor, and Wu, Fei, editor

Published
2015
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181. Effective Sampling of Points of Interests on Maps Based on Road Networks

Author

Zhou, Ziting, Zhao, Pengpeng, Sheng, Victor S., Xu, Jiajie, Li, Zhixu, Wu, Jian, Cui, Zhiming, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Dong, Xin Luna, editor, Yu, Xiaohui, editor, Li, Jian, editor, and Sun, Yizhou, editor

Published
2015
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182. RPCV: Recommend Potential Customers to Vendors in Location-Based Social Network

Author

Liu, Yuanliu, Zhao, Pengpeng, Sheng, Victor S., Li, Zhixu, Liu, An, Wu, Jian, Cui, Zhiming, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Dong, Xin Luna, editor, Yu, Xiaohui, editor, Li, Jian, editor, and Sun, Yizhou, editor

Published
2015
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183. Batch Mode Active Learning for Networked Data with Optimal Subset Selection

Author

Xu, Haihui, Zhao, Pengpeng, Sheng, Victor S., Liu, Guanfeng, Zhao, Lei, Wu, Jian, Cui, Zhiming, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Dong, Xin Luna, editor, Yu, Xiaohui, editor, Li, Jian, editor, and Sun, Yizhou, editor

Published
2015
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184. Effective Spatial Keyword Query Processing on Road Networks

Author

Fang, Hailin, Zhao, Pengpeng, Sheng, Victor S., Wu, Jian, Xu, Jiajie, Liu, An, Cui, Zhiming, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Sharaf, Mohamed A., editor, Cheema, Muhammad Aamir, editor, and Qi, Jianzhong, editor

Published
2015
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185. TK-SK: Textual-Restricted Spatial Keyword Query on Road Networks

Author

Kuang, Xiaopeng, Zhao, Pengpeng, Sheng, Victor S., Wu, Jian, Li, Zhixu, Liu, Guanfeng, Cui, Zhiming, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Sharaf, Mohamed A., editor, Cheema, Muhammad Aamir, editor, and Qi, Jianzhong, editor

Published
2015
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186. Scalable Top- Spatial Image Search on Road Networks

Author

Zhao, Pengpeng, Kuang, Xiaopeng, Sheng, Victor S., Xu, Jiajie, Wu, Jian, Cui, Zhiming, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Renz, Matthias, editor, Shahabi, Cyrus, editor, Zhou, Xiaofang, editor, and Cheema, Muhammad Aamir, editor

Published
2015
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187. Immune Centroids Over-Sampling Method for Multi-Class Classification

Author

Ai, Xusheng, Wu, Jian, Sheng, Victor S., Zhao, Pengpeng, Yao, Yufeng, Cui, Zhiming, Goebel, Randy, Series editor, Tanaka, Yuzuru, Series editor, Wahlster, Wolfgang, Series editor, Cao, Tru, editor, Lim, Ee-Peng, editor, Zhou, Zhi-Hua, editor, Ho, Tu-Bao, editor, Cheung, David, editor, and Motoda, Hiroshi, editor

Published
2015
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197. Edge-enhanced Global Disentangled Graph Neural Network for Sequential Recommendation

Author

Li, Yunyi, Hao, Yongjing, Zhao, Pengpeng, Liu, Guanfeng, Liu, Yanchi, Sheng, Victor S., Zhou, Xiaofang, Li, Yunyi, Hao, Yongjing, Zhao, Pengpeng, Liu, Guanfeng, Liu, Yanchi, Sheng, Victor S., and Zhou, Xiaofang

Abstract

Sequential recommendation has been a widely popular topic of recommender systems. Existing works have contributed to enhancing the prediction ability of sequential recommendation systems based on various methods, such as recurrent networks and self-attention mechanisms. However, they fail to discover and distinguish various relationships between items, which could be underlying factors which motivate user behaviors. In this article, we propose an Edge-Enhanced Global Disentangled Graph Neural Network (EGD-GNN) model to capture the relation information between items for global item representation and local user intention learning. At the global level, we build a global-link graph over all sequences to model item relationships. Then a channel-aware disentangled learning layer is designed to decompose edge information into different channels, which can be aggregated to represent the target item from its neighbors. At the local level, we apply a variational auto-encoder framework to learn user intention over the current sequence. We evaluate our proposed method on three real-world datasets. Experimental results show that our model can get a crucial improvement over state-of-the-art baselines and is able to distinguish item features.

Published
2023

198. Feature-Level Deeper Self-Attention Network With Contrastive Learning for Sequential Recommendation

Author

Hao, Yongjing, Zhang, Tingting, Zhao, Pengpeng, Liu, Yanchi, Sheng, Victor S., Xu, Jiajie, Liu, Guanfeng, Zhou, Xiaofang, Hao, Yongjing, Zhang, Tingting, Zhao, Pengpeng, Liu, Yanchi, Sheng, Victor S., Xu, Jiajie, Liu, Guanfeng, and Zhou, Xiaofang

Abstract

Sequential recommendation, which aims to recommend next item that the user will likely interact in a near future, has become essential in various Internet applications. Existing methods usually consider the transition patterns between items, but ignore the transition patterns between features of items. We argue that only the item-level sequences cannot reveal the full sequential patterns, while explicit and implicit feature-level sequences can help extract the full sequential patterns. Meanwhile, the item-level sequential recommendation also suffers from limited supervised signal issues. In this paper, we propose a novel model Feature-level Deeper Self-Attention Network with Contrastive Learning (FDSA-CL) for sequential recommendation. Specifically, FDSA-CL first integrates various heterogeneous features of items into feature-level sequences with different weights through a vanilla attention mechanism. After that, FDSA-CL applies separated self-attention blocks on item-level sequences and feature-level sequences, respectively, to model item transition patterns and feature transition patterns. Moreover, we propose contrastive learning and item feature recommendation tasks to capture the embedding commonality and further utilize the beneficial interaction among the two levels, so as to alleviate the sparsity of the supervised signal and extract the most critical information. Finally, we jointly optimize the above tasks. We evaluate the proposed model using two real-world datasets and experimental results show that our model significantly outperforms the state-of-the-art approaches.

Published
2023

200. Learnable Model Augmentation Contrastive Learning for Sequential Recommendation

Author

Hao, Yongjing, Zhao, Pengpeng, Xian, Xuefeng, Liu, Guanfeng, Zhao, Lei, Liu, Yanchi, Sheng, Victor S., Zhou, Xiaofang, Hao, Yongjing, Zhao, Pengpeng, Xian, Xuefeng, Liu, Guanfeng, Zhao, Lei, Liu, Yanchi, Sheng, Victor S., and Zhou, Xiaofang

Abstract

Sequential Recommendation (SR) methods play a crucial role in recommender systems, which aims to capture users' dynamic interest from their historical interactions. Recently, Contrastive Learning (CL), which has emerged as a successful method for sequential recommendation, utilizes various data augmentations to generate contrastive views to mine supervised signals from data to alleviate data sparsity issues. However, most existing sequential data augmentation methods may destroy semantic sequential interaction characteristics. Meanwhile, they often adopt random operations when generating contrastive views leading to suboptimal performance. To this end, in this paper, we propose a Learnable Model Augmentation Contrastive learning for sequential Recommendation (LMA4Rec). Specifically, LMA4Rec first takes the model-based augmentation method to generate constructive views. Then, LMA4Rec uses Learnable Bernoulli Dropout (LBD) to implement learnable model augmentation operations. Next, contrastive learning is used between the contrastive views to extract supervised signals. Furthermore, a novel multi-positive contrastive learning loss alleviates the supervised sparsity issue. Finally, experiments on public datasets show that our LMA4Rec method effectively improved sequential recommendation performance compared with the state-of-the-art baseline methods. IEEE

Published
2023

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