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arXiv 2024年

作者： Gong, Jibing Peng, Jiquan Qu, Jin Du, ShuYing Wang, Kaiyu School of Information Science and Engineering Yanshan University The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province China

Detecting Twitter Bots is crucial for maintaining the integrity of online discourse, safeguarding democratic processes, and preventing the spread of malicious propaganda. However, advanced Twitter Bots today often employ sophisticated feature manipulation and account farming techniques to blend seamlessly with genuine user interactions, posing significant challenges to existing detection models. In response to these challenges, this paper proposes a novel Twitter Bot Detection framework called BotSAI. This framework enhances the consistency of multimodal user features, accurately characterizing various modalities to distinguish between real users and bots. Specifically, the architecture integrates information from users, textual content, and heterogeneous network topologies, leveraging customized encoders to obtain comprehensive user feature representations. The heterogeneous network encoder efficiently aggregates information from neighboring nodes through oversampling techniques and local relationship transformers. Subsequently, a multi-channel representation mechanism maps user representations into invariant and specific subspaces, enhancing the feature vectors. Finally, a self-attention mechanism is introduced to integrate and refine the enhanced user representations, enabling efficient information interaction. Extensive experiments demonstrate that BotSAI outperforms existing state-of-the-art methods on two major Twitter Bot Detection benchmarks, exhibiting superior performance. Additionally, systematic experiments reveal the impact of different social relationships on detection accuracy, providing novel insights for the identification of social bots. © 2024, CC BY.

关键词： Tweets

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Real-time simulation of action-driven 3D subtle facial expressions

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Journal of Computational Information systems 2014年第5期10卷 2137-2144页

作者： Tang, Yong Liang, Haiyan Guo, Dan Lv, Mengya Zhao, Jing College of Information Science and Engineering Yanshan University Qinhuangdao 066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao 066004 China

While the large-scale deformations such as the Laplacian deformation method could not synthesize new expressional details, this paper proposes a method for simulating different subtle facial expressions based on the Kinect acquisition system and the facial expressional reconstruction technique. Firstly, we generate the coarse skin textures with pore details and those normal maps. Secondly, we use blending wrinkle maps to render animated details on a human face based on a GPU shader program. Finnaly, according to the action units captured by the Kinect, we calculate the dynamic texture mapping and the weight of wrinkles, and apply the wrinkles function to simulate the motion of wrinkles. Experimental results show that our method can generate facial details in real time. The method suits the fields such as digital entertainment and video conferences. © 2014 Binary Information Press.

关键词： Blending

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IMSPMIS-Stream: incremental mining of top-k short sequential pattern over multiple item set streams

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Journal of Computational Information systems 2014年第6期10卷 2305-2312页

作者： Hao, Xiaobing Han, Gaowei Chen, Yuping Wang, Peilong Ren, Jiadong College of Information Science and Engineering Yanshan University Qinhuangdao 066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao 066004 China

Many previous algorithms in data streams are about single stream, which can only process single items. The algorithms about data streams are always extended by sequential pattern algorithms about static database, they can't satisfy the requirement of scanning streams only once and online mining. Moreover, top-k short sequential pattern hasn't been studied yet. Therefore, IMSPMIS-Stream algorithm is proposed to mine online multiple streams with the single scan by the incremental method in this paper. The streams are continuous transactions, which is also called item set streams. Several streams are mined interactively based on sliding windows at the same time. An approach to count the support of every sub item set is designed to get the support during one scan. The short sequential pattern is deffned, whose item sets are shorter than all the item sets with the same occurring time. When the arrival transactions reach to the capacity of the window, sequential patterns will be output by combining top-k short item sets. The experiments show that the IMSPMIS-Stream algorithm overcomes the memory limitation and its execution time is reduced compared with some previous algorithms under the same conditions. Copyright © 2014 Binary Information Press.

关键词： Information systems

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Research of Signed Networks Community Detection Based on the Tightness of Common Neighbors 6

Research of Signed Networks Community Detection Based on the...

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6th International Conference on Digital Home, ICDH 2016

作者： Xinzhuan, Hu Guo, Jingfeng Chen, Xiao Zhao, Xiaomeng School of Economics and Management Yanshan University Hebei Ave. Qinhuangdao China College of Information Science and Engineering Yanshan University Hebei Ave. Qinhuangdao China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Hebei Ave. Qinhuangdao China

ISBN: (纸本)9781509044009

According to the characteristics of positive and negative edge of signed networks, a new signed networks community detection algorithm BTCN-SNCD (Signed networks Community Detection Based on the Tightness of Common Neighbors) is proposed based on the tightness of common neighbors. Firstly, in view of the shortcomings of the traditional local similarity metrics only considering the number of common neighbors, the contribution degree and the tightness of common neighbors are defined, which can discover the initial communities with tighter structure. Secondly, for the overlapping nodes exist in initial communities, we propose overlap coefficient and community density to remerge the initial communities, thus improving community detection accuracy. Finally, the correctness and efficiency of the BTCN-SNCD algorithm are verified through a comparison with classic signed networks community detection algorithms. © 2016 IEEE.

关键词： Signal detection

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Mobile APP User Attribute Prediction by Heterogeneous Information Network Modeling 5th

Mobile APP User Attribute Prediction by Heterogeneous Inform...

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5th International Conference on Dependability in Sensor, Cloud, and Big Data systems and Applications, DependSys 2019

作者： Zhang, Hekai Gong, Jibing Teng, Zhiyong Wang, Dan Wang, Hongfei Du, Linfeng Bhuiyan, Zakirul Alam School of Information Science and Engineering Yanshan University Qinhuangdao066004 China The Key Lab for Computer Virtual Technology and System Integration Yanshan University Qinhuangdao066004 China School of Computer Science and Engineering Beihang University Beijing100191 China Shenyuan Honors College Beihang University Beijing100191 China State Key Lab of Mathematical Engineering and Advanced Computing Wuxi214000 China Department of Computer and Information Sciences Fordham University New York United States

ISBN: (纸本)9789811513039

User-based attribute information, such as age and gender, is usually considered as user privacy information. It is difficult for enterprises to obtain user-based privacy attribute information. However, user-based privacy attribute information has a wide range of applications in personalized services, user behavior analysis and other aspects. Although many scholars have made achievements in user attribute prediction and other related fields, there are still two main problems that impede further improvement on the accuracy of classification: (1) Traditional machine learning classification merely takes each object as a single individual, ignoring the relationship between them;(2) At present, the popular Heterogeneous Path-Mine Information Network only considers whether the user has a relationship with the attributes of other nodes, rather than the degree of correlation of the attributes. It employs a linear regression model to fit the weight of meta-path, which is highly sensitive to outliers. To solve the above two problems, this paper advances the HetPathMine model and puts forward TPathMine model. With applying the number of clicks of attributes under each node to express the user’s emotional preference information, optimizations of the solution of meta-path weight are also presented. Based on meta-path in heterogeneous information networks, the new model integrates all relationships among objects into isomorphic relationships of classified objects. Matrix is used to realize the knowledge dissemination of category knowledge among isomorphic objects. The experimental results show that: (1) the prediction of user attributes based on heterogeneous information networks can achieve higher accuracy than traditional machine learning classification methods;(2) TPathMine model based on the number of clicks is more accurate in classifying users of different age groups, and the weight of each meta-path is consistent with human intuition or the real world situation. © 2019, Spr

关键词： Forecasting

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Mining time-interval weighted closed sequential patterns based on memory indexing

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Journal of Computational Information systems 2014年第1期10卷 293-300页

作者： Zeng, Qiang Chen, Dengxi Ren, Jiadong Han, Gaowei College of Information Science and Engineering Yanshan University Qinhuangdao 066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao 066004 China

General weighted sequential pattern mining algorithms ignore or do not make good use of the time and time-interval information of data elements. Besides some algorithms require to scan the database many times or build temporary databases. To solve these problems, we propose a memory-based algorithm MITWCSpan (Memory Indexing for time-interval Weighted Closed Sequential pattern mining) for timeinterval weighted closed sequential pattern mining. The algorithm takes full account of the importance of the time-interval of data elements. Moreover, an improved index set based on time-interval, p-tidx, is defined. During mining process, the algorithm adopts the find-then-index technique recursively to find the items which can constitute a time-interval weighted sequential pattern and construct p-tidx for the possible sequential pattern. Finally the algorithm uses closing detection to get the whole time-interva l weighted closed sequential patterns. The experimental results show that the algorithm is more efficient in finding more important sequential patterns. Copyright © 2014 Binary Information Press.

关键词： Indexing (of information)

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An energy-efficient routing algorithm of zigbee network

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Journal of Computational Information systems 2012年第23期8卷 9873-9879页

作者： Liu, Yongshan Chen, Wei Shang, Xuehui Lv, Yongteng Han, Yuanyuan Liu, Chang College of Information Science and Engineering Yanshan University Qinhuangdao 066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao 066004 China

As regard to the case of extending the lifetime of zigbee network, the defination of node's boundary is proposed. First, all the information for node's boundary is stored when zigbee network is built. Then, the packet between the nodes which are in the node's boundary is transferred directly. The packet between the nodes whose boundaries are adjacent is transferred through the node with maximum energy. The exprimental results show that the new algorithm can efficiently extend the lifetime of zigbee network. ©2012 by Binary Information Press.

关键词： Zigbee

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Robust recommendation algorithm based on user rating matrix block and modified LTS-estimator

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Journal of Information and Computational Science 2014年第6期11卷 1889-1898页

作者： Xu, Yuchen Liu, Zhen Zhang, Fuzhi School of Information Science and Engineering Yanshan University Qinhuangdao 066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao 066004 China

The most widely-used collaborative recommendation algorithms are vulnerable to shilling attacks. To this end, in this paper we propose a robust recommendation algorithm based on user rating matrix block and modified LTS-estimator. Firstly, we construct user rating matrix blocks using user rating matrix block algorithm based on k-median clustering. Secondly, we apply the modified LTS-estimator to matrix factorization model in order to produce user feature matrix and item feature matrix. Finally, we devise a robust recommendation algorithm to generate recommendations for the target users. Experimental results on the MovieLens dataset show that the proposed algorithm outperforms the existing methods in terms of both the prediction accuracy and robustness. © 2014 Binary Information Press.

关键词： Matrix factorization

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Density-based clustering for evolving uncertain data stream

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Journal of Computational Information systems 2014年第1期10卷 419-426页

作者： He, Haitao Zhao, Jintian The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao 066004 China College of Information Science and Engineering Yanshan University Qinhuangdao 066004 China

The current clustering algorithms for evolving uncertain data stream are sensitive to user specified threshold, and unstable in noise processing. In this paper, DUStream is presented, a density-based algorithm for discovering clusters in evolving uncertain data stream. Probability distance is introduced as a new similarity measure, giving consideration to probability attribute and distance attribute. Probability Radius is used as a self-adaption dynamic threshold to reduce the effect of user specified input. The experimental results demonstrate the effectiveness and Effciency of the algorithm on Artificial and real data sets. Copyright © 2014 Binary Information Press.

关键词： Probability

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virtual grid-based clustering of uncertain data on vulnerability database

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Journal of Convergence Information technology 2012年第20期7卷 429-438页

作者： Dong, Jun Cao, Mengmeng Huang, Guoyan Ren, Jiadong College of Information Science and Engineering Yanshan University Qinhuangdao 066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao 066004 China

Most existing vulnerability taxonomy classifies vulnerabilities by their idiosyncrasies, weaknesses, flaws and faults et al. The disadvantage of the taxonomy is that the classification standard is not unified and there is overlap classification phenomenon in vulnerability taxonomy. In order to solve the problem, we will propose an algorithm VUNClique, virtual Grid-based Clustering of Uncertain Data on vulnerability database. Firstly, this paper transforms the vulnerability database into uncertain dataset using the existing vulnerability database pretreatment model. Secondly, we define a virtual grid structure, the cells are divided into real cells and virtual cells, but only the real cells which contain data objects stored in memory. The probability attribute value similarity is defined to deal with the similarity of non-numeric attributes, which compares the number of non-numeric attributes with the same value between tuples to measure the similarity. We provide a secondary partition algorithm to improve the similarity between the tuples in the same cell, the algorithm merges a tuple into it's high-density neighbor cell which has the maximum value of probability attribute value similarity with it. Then, a novel identify cluster algorithm is provided to cluster the high-density real cells. It can identify clusters of arbitrary shapes by traversing real cells twice. Finally, performance experiments over the uncertain dataset transformed by NVD vulnerability database. The experiments results show that VUNClique can find clusters of arbitrary shapes, and greatly improve the efficiency of clustering.

关键词： Cells

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