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Accumulative Time Based Ranking Method to Reputation Evaluation in Information Networks

Journal of computer Science & Technology 2022年第4期37卷 960-974页

作者： Hao Liao Qi-Xin Liu Ze-Cheng Huang Ke-Zhong Lu Chi Ho Yeung Yi-Cheng Zhang National Engineering Laboratory on Big Data System Computing Technology College of Computer Science and SoftwareEngineeringShenzhen UniversityShenzhen 518060China Guangdong Province Key Laboratory of Popular High Performance Computers Shenzhen UniversityShenzhen 518060China Guangdong Province Engineering Center of China-Made High Performance Data Computing System College of ComputerScience and Software EngineeringShenzhen UniversityShenzhen 518060China Institute of Big Data Intelligent Management and Decision Shenzhen UniversityShenzhen 518060China Department of Science and Environmental Studies The Education University of Hong KongHong Kong 999077China Department of Physics University of FribourgFribourg 1700Switzerland

Due to over-abundant information on the Web, information filtering becomes a key task for online users to obtain relevant suggestions and how to extract the most related item is always a key topic for researchers in various fields. In this paper, we adopt tools used to analyze complex networks to evaluate user reputation and item quality. In our proposed Accumulative Time Based Ranking (ATR) algorithm, we take into account the growth record of the network to identify the evolution of the reputation of users and the quality of items, by incorporating two behavior weighting factors which can capture the hidden facts on reputation and quality dynamics for each user and item respectively. Our proposed ATR algorithm mainly combines the iterative approach to rank user reputation and item quality with temporal dependence compared with other reputation evaluation methods. We show that our algorithm outperforms other benchmark ranking algorithms in terms of precision and robustness on empirical datasets from various online retailers and the citation datasets among research publications. Therefore, our proposed method has the capability to effectively evaluate user reputation and item quality.

关键词： temporal network behavior dynamics reputation evaluation ranking algorithm

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Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection 24

Dual-Modeling Decouple Distillation for Unsupervised Anomaly...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Liu, Xinyue Wang, Jianyuan Leng, Biao Zhang, Shuo School of Computer Science and Engineering Beihang University Beijing China Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China Beijing Key Lab of Traffic Data Analysis and Mining School of Computer & Technology Beijing Jiaotong University Beijing China

ISBN: (纸本)9798400706868

Knowledge distillation based on student-teacher network is one of the mainstream solution paradigms for the challenging unsupervised Anomaly Detection task, utilizing the difference in representation capabilities of the teacher and student networks to implement anomaly localization. However, over-generalization of the student network to the teacher network may lead to negligible differences in representation capabilities of anomaly, thus affecting the detection effectiveness. Existing methods address the possible over-generalization by using differentiated students and teachers from the structural perspective or explicitly expanding distilled information from the content perspective, which inevitably results in an increased likelihood of underfitting of the student network and poor anomaly detection capabilities in anomaly center or edge. In this paper, we propose Dual-Modeling Decouple Distillation (DMDD) for the unsupervised Anomaly Detection. In DMDD, a Decouple Student-Teacher Network is proposed to decouple the initial student features into normality and abnormality features. We further introduce Dual-Modeling Distillation based on normal-anomalous image pairs, fitting normality features of anomalous image and the teacher features of the corresponding normal image, widening the distance between abnormality features and the teacher features in anomalous regions. Synthesizing these two distillation ideas, we achieve anomaly detection which focuses on both edge and center of anomaly. Finally, a Multi-perception Segmentation Network is proposed to achieve focused anomaly map fusion based on multiple attention. Experimental results on MVTec AD show that DMDD surpasses SOTA localization performance of previous knowledge distillation-based methods, reaching 98.85% on pixel-level AUC and 96.13% on PRO. © 2024 ACM.

关键词： Students

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GACP:graph neural networks with ARMA flters and a parallel CNN for hyperspectral image classification

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International Journal of Digital Earth 2023年第1期16卷 1770-1800页

作者： Jing Yang Jie Sun Yaping Ren Shaobo Li Shujie Ding Jianjun Hu School of Mechanical Engineering Guizhou UniversityGuiyangPeople’s Republic of China State Key Laboratory of Public Big Data Guizhou UniversityGuiyangPeople’s Republic of China School of Intelligent Systems Science and Engineering Jinan UniversityZhuhaiPeople’s Republic of China Department of Computer Science and Engineering University of South CarolinaColumbiaSCUSA

In recent years,the use of convolutional neural networks(CNNs)and graph neural networks(GNNs)to identify hyperspectral images(HSIs)has achieved excellent results,and such methods are widely used in agricultural remote sensing,geological exploration,and marine remote *** many generalization classification algorithms are designed for the purpose of learning a small number of samples,there is often a problem of a low utilization rate of position information in the empty spectral *** on this,a GNN with an autoregressive moving average(ARMA)-based smoothingfilter samples the node information in the null spectral domain and then captures the spatial information at the pixel level via spatial feature convolution;then,the null spectral domain position information lost by the CNN is located by a coordinate attention(CA)***,autoregressive,spatial convolution,and CA mechanisms are combined into multiscale features to enhance the learning capacity of the network for tiny *** conducted on the widely used Indian Pines(IP)dataset,the Botswana(BS)dataset,Houton 2013(H2013),and the WHU-Hi-HongHu(WHU)benchmark HSI dataset demonstrate that the proposed GACP technique can perform classification work with good accuracy even with a small number of training examples.

关键词： Graph neural networks word convolutional neural networks hyperspectral image classification attention mechanisms

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Link Prediction for Opportunistic Networks Based on Hybrid Similarity Metrics and E-LSTM-D Models 24th

Link Prediction for Opportunistic Networks Based on Hybrid...

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24th International Conference on Web Information Systems engineering, WISE 2023

作者： Yang, Xiaoying Li, Lijie Xu, Gang Song, Ming Zhang, Feng College of Computer Science Inner Mongolia University Hohhot010021 China Inner Mongolia A.R. Key Laboratory of Data Mining and Knowledge Engineering Inner Mongolia University Hohhot010021 China School of Computer and Information Technology Shanxi University Taiyuan030000 China

ISBN: (纸本)9789819972531

Link prediction is a crucial issue in opportunistic networks routing research. Static link prediction methods ignore the historical information of network evolution, which affects the prediction accuracy. In this paper, we propose a link prediction method (HS-LSTM) that combines hybrid similarity metrics and the E-LSTM-D model. The approach extracts structural features using hybrid similarity metrics, constructs a structural feature matrix, and applies the E-LSTM-D model to extract temporal features and predict the links that have not yet appeared in the network. Our experiments on three real datasets of opportunistic networks (Infocom5, MIT, and Haggle) shows that our proposed method outperforms the baseline method in terms of prediction accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Learning to Communicate Among Agents for Large-Scale Dynamic Path Planning With Genetic Programming Hyperheuristic

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1269-1283页

作者： Liao, Xiao-Cheng Hu, Xiao-Min Chen, Xiang-Ling Mei, Yi Jia, Ya-Hui Chen, Wei-Neng Victoria University of Wellington Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China Hanyang University Department of Electrical and Electronic Engineering Ansan15588 Korea Republic of South China University of Technology School of Future Technology Guangzhou510006 China Pazhou Lab Guangzhou510005 China South China University of Technology School of Computer Science and Engineering State Key Laboratory of Subtropical Building and Urban Science Guangzhou510006 China

Genetic programming hyperheuristic (GPHH) has recently become a promising methodology for large-scale dynamic path planning (LDPP) since it can produce reusable heuristics rather than disposable solutions. However, in this methodology, the extracted local and decentralized heuristic for agents that lack a global systemic view sometimes may be problematic. Therefore, a new challenge is to strike a balance between conciseness to improve generalization ability and incorporation of more global information to obtain better performance. In this work, we target the LDPP problem and propose a communication learning mechanism (ComLGP) for GPHH to address the above difficulties. In ComLGP, a communication function is introduced to serve as a communication protocol and exist in the form of an extra terminal in GPHH. Compared to the classic terminals which are fixed in genetic programing, this communication function undergoes optimization along with the evolutionary process of GPHH. In this way, the communication function can be learned which enables agents to communicate without a predefined communication protocol. Then, a caching and lazy updating mechanism for ComLGP is presented to accelerate the calculation of communication content. Last, we verified our method on 22 scenarios including two real world road networks. The experimental results demonstrate that the proposed ComLGP can successfully learn to communicate. Although in the absence of any manually designed communication features, ComLGP is capable of achieving performance competitive to the state-of-the-art method that employs a predefined communication protocol and outperforms the remaining compared methods in most scenarios. © 2020 IEEE.

关键词： Genetic programming

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Unveiling Vulnerability of Self-Attention 30

Unveiling Vulnerability of Self-Attention

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Liong, Khai Jiet Wu, Hongqiu Zhao, Hai Xiaoshan, Li Minjie, Bian Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Shanghai Technology Innovation Department Shanghai Data Group Co. Ltd. China Shanghai Data Group Co. Ltd. China

ISBN: (纸本)9782493814104

Pre-trained language models (PLMs) are shown to be vulnerable to minor word changes, which poses a big threat to real-world systems. While previous studies directly focus on manipulating word inputs, they are limited by their means of generating adversarial samples, lacking generalization to versatile real-world attack. This paper studies the basic structure of transformer-based PLMs, the self-attention (SA) mechanism. (1) We propose a powerful perturbation technique HackAttend, which perturbs the attention scores within the SA matrices via meticulously crafted attention masks. We show that state-of-the-art PLMs fall into heavy vulnerability that minor attention perturbations (1%) can produce a very high attack success rate (98%). Our paper expands the conventional text attack of word perturbations to more general structural perturbations. (2) We introduce S-Attend, a novel smoothing technique that effectively makes SA robust via structural perturbations. We empirically demonstrate that this simple yet effective technique achieves robust performance on par with adversarial training when facing various text attackers. Code is publicly available at ***/liongkj/HackAttend. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Semantics

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A novel CAPTCHA scheme based on facial expression reconstruction

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International Journal of Electronic Business 2020年第4期15卷 368-388页

作者： Moradi, Mohammad Keyvanpour, Mohammad Reza Data Mining Laboratory Department of Computer Engineering Alzahra University Tehran Iran Department of Computer Engineering Alzahra University Tehran Iran

Despite some technical and conceptual criticism on the CAPTCHA and its claimed effectiveness, such human interaction proofs (HIPs) are still in the centre of attention. With the aim of designing a novel, efficient and user-friendly CAPTCHA, the facial expression reconstruction CAPTCHA, FERCHA, is proposed. The distinguishing challenge in this schema is reconstruction of a facial expression related to the perceived emotion in a game-like manner. In one side, the weakness of current machines in performing cognitive tasks has been leveraged to introduce a strong CAPTCHA. Also, as the byproduct, the FERCHA schema can be used as a crowdsourcing platform in order to collect human-generated semantic annotation and interpretation of text, emojis and images. The experimental results (including theoretical analysis and user acceptance evaluation) proved the efficacy and robustness of the presented idea. Moreover, it is found that providing humans with user-friendly experience could improve the success rate and satisfaction level. Copyright © 2020 Inderscience Enterprises Ltd.

关键词： Human computer interaction

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Harmonic Long-Range Backscatter with Frequency-Shifted Lightweight Tag 100

Harmonic Long-Range Backscatter with Frequency-Shifted Light...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Lin, Junliang Zhang, Xiannan Xu, Rongtao Wang, Gongpu Quek, Tony Q. S. Beijing Jiaotong University Beijing Key Laboratory of Transportation Data Analysis and Mining Beijing China Beijing Jiaotong University School of Electronic and Information Engineering Beijing China Singapore University of Technology and Design Department of Information Systems Technology and Design Singapore

ISBN: (纸本)9798331517786

In this paper, we introduce a simplified long-range (LoRa) backscatter system that enables a lightweight tag to communicate with a remote transceiver using chirp carrier and harmonic backscatter. The key idea is twofold: first, we delegate the chirp waveform generation from the tag to the transceiver, thereby maintaining lightweight tag design;second, we introduce a frequency shift on the tag during its carrier modulation to create harmonic backscatter, thereby mitigating self-interference. To refine the framework, we first detail the system model, including the design for harmonic backscatter. We then present an efficient method to address synchronization and detection issues at the transceiver. Finally, we prototype the proposed system and evaluate its performance. Experimental results demonstrate that in a corridor environment with a carrier power of 0 dBm, the tag can backscatter a signal from the transceiver at a maximum bit rate of 4 kb/s over a distance of 50 meters. © 2024 IEEE.

关键词： Harmonic analysis

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Instruction-Driven Game Engine: A Poker Case Study

Instruction-Driven Game Engine: A Poker Case Study

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Wu, Hongqiu Liu, Xingyuan Wang, Yan Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China Tencent China

ISBN: (纸本)9798891761674

The Instruction-Driven Game Engine (IDGE) project aims to democratize game development by enabling a large language model (LLM) to follow free-form game descriptions and generate game-play processes. The IDGE allows users to create games simply by natural language instructions, which significantly lowers the barrier for game development. We approach the learning process for IDGEs as a Next State Prediction task, wherein the model autoregressively predicts the game states given player actions. The computation of game states must be precise;otherwise, slight errors could corrupt the game-play experience. This is challenging because of the gap between stability and diversity. To address this, we train the IDGE in a curriculum manner that progressively increases its exposure to complex scenarios. Our initial progress lies in developing an IDGE for Poker, which not only supports a wide range of poker variants but also allows for highly individualized new poker games through natural language inputs. This work lays the groundwork for future advancements in transforming how games are created and played. © 2024 Association for Computational Linguistics.

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GBKM: A New Genetic Based K-Means Clustering Algorithm 7

GBKM: A New Genetic Based K-Means Clustering Algorithm

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7th International Conference on Web Research, ICWR 2021

作者： Mardi, Mahnaz Keyvanpour, Mohammad Reza Alzahra University Data Mining Lab Faculty of Engineering Department of Computer Engineering Tehran Iran

ISBN: (纸本)9781665404266

Clustering is an unsupervised classification method that focused on grouping data into clusters. The objects in each cluster are very similar but different from the objects in the other clusters. As clustering methods deal with the massive amount of information, many intelligent software agents have been widely utilized clustering techniques to filter, retrieve, and categorize documents that exist on the World Wide Web. Web mining is generally classified under data mining. In data mining, one of the significant clustering centroid-based partitioning methods is the K-Means algorithm. One of the K-Means algorithm's challenges is its extreme sensitivity to initial cluster centers' choice, which may yield get stuck in the local optimum if the initial centers are selected randomly. A variant of the K-Means method is the K-Means++ algorithm, which improves the algorithm's performance by smart choices of initialization of the cluster centroids. Evolutionary techniques, widely utilized for optimizing clustering methods by providing their prerequisite parameters. The Genetic Algorithm is stochastic and population-based, that applied in optimization problem-solving. This paper proposed a Genetic-based K-Means (GBKM) clustering algorithm where the clusters' centroids are encoded by chromosomes rather than random initial cluster centroids. The best cluster centers gave by the Genetic algorithm that maximizes the fitness function, as initial points of the K-Means algorithm. The results show this model helps increase the K-Means algorithm's performance by appropriate choice of initialization of the cluster centroids, compared to four other clustering algorithms. © 2021 IEEE.

关键词： Genetic algorithms

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