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arXiv 2021年

作者： Gao, Hao Wang, Yongqing Lyu, Shanshan Shen, Huawei Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

Nowadays online users prefer to join multiple social media for the purpose of socialized online service. The problem anchor link prediction is formalized to link user data with the common ground on user profile, content and network structure across social networks. Most of the traditional works concentrated on learning matching function with explicit or implicit features on observed user data. However, the low quality of observed user data confuses the judgment on anchor links, resulting in the matching collision problem in practice. In this paper, we explore local structure consistency and then construct a matching graph in order to circumvent matching collisions. Furthermore, we propose graph convolution networks with mini-batch strategy, efficiently solving anchor link prediction on matching graph. The experimental results on three real application scenarios show the great potentials of our proposed method in both prediction accuracy and efficiency. In addition, the visualization of learned embeddings provides us a qualitative way to understand the inference of anchor links on the matching graph. © 2021, CC BY-NC-SA.

关键词： Convolution

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Make It Possible: Multilingual Sentiment Analysis without Much Prior Knowledge

Make It Possible: Multilingual Sentiment Analysis without Mu...

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Zheng Lin Xiaolong Jin Xueke Xu Yuanzhuo Wang Songbo Tan Xueqi Cheng CAS Key Laboratory on Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781479941421

Sentiment analysis is a hard problem, while multilingual sentiment analysis is even harder due to the different expression styles in different languages. Although many methods for multilingual sentiment analysis have been developed in the open literature, most of them suffer from two major problems. The first is their excessive dependence on external tools or resources (e.g., Machine translation systems or bilingual dictionaries), which may not be readily obtained, especially for minority languages, The second is conflictive sentiments, i.e., The sentiment polarity of some parts of a text is inconsistent with its overall sentiment polarity. It is observed that in a product or service review there usually exist a few sentences which play a more important role in determining its sentiment polarity, as compared to others. Therefore, differentiating key sentences from trivial ones may be helpful to improve sentiment analysis. Inspired by this observation in this paper we propose a novel framework to estimate the sentiment polarity of reviews by virtue of opinion lexica and key sentences automatically extracted from unlabelled data. This framework cannot only overcome the problem of excessive dependence on external resources, but also is able to capture the overall sentiment polarity of reviews. Experimental results on realistic review datasets demonstrate that the proposed framework is effective and competitive with the representative baselines.

关键词： Sentiment analysis Dictionaries data mining Training Algorithm design and analysis Google Feature extraction

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On layer normalization in the transformer architecture 37

On layer normalization in the transformer architecture

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37th International Conference on Machine Learning, ICML 2020

作者： Xiong, Ruibin Yang, Yunchang He, Di Zheng, Kai Zheng, Shuxin Xing, Chen Zhang, Huishuai Lan, Yanyan Wang, Liwei Liu, Tie-Yan CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technolog Chinese Academy of Sciences China University of Chinese Academy of Sciences China Center for Data Science Peking University Beijing Institute of Big Data Research China Key Laboratory of Machine Perception MOE School of EECS Peking University China Microsoft Research College of Computer Science Nankai University China

ISBN: (纸本)9781713821120

The Transformer is widely used in natural language processing tasks. To train a Transformer however, one usually needs a carefully designed learning rate warm-up stage, which is shown to be crucial to the final performance but will slow down the optimization and bring more hyperparameter tunings. In this paper, we first study theoretically why the learning rate warm-up stage is essential and show that the location of layer normalization matters. Specifically, we prove with mean field theory that at initialization, for the original-designed Post-LN Transformer, which places the layer normalization between the residual blocks, the expected gradients of the parameters near the output layer are large. Therefore, using a large learning rate on those gradients makes the training unstable. The warm-up stage is practically helpful for avoiding this problem. On the other hand, our theory also shows that if the layer normalization is put inside the residual blocks (recently proposed as Pre-LN Transformer), the gradients are well-behaved at initialization. This motivates us to remove the warm-up stage for the training of Pre-LN Transformers. We show in our experiments that Pre-LN Transformers without the warm-up stage can reach comparable results with baselines while requiring significantly less training time and hyper-parameter tuning on a wide range of applications. Copyright 2020 by the author(s).

关键词： Mean field theory

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Type-Representation Enhanced Event Detection via Structure Encoder 1

Type-Representation Enhanced Event Detection via Structure E...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Feng, Yuming Huang, Linmeng Liang, Haozhe Jin, Xiaolong Guan, Saiping Guo, Jiafeng School of Computer Science and Technology University of Chinese Academy of Sciences China Institute of Computing Technology Chinese Academy of Sciences Key Lab of Network Data Science and Technology China Institute of Systems Engineering Academy of Military Science Beijing China

ISBN: (纸本)9798350358780

Event detection, aiming to detect whether a sentence contains the type of events of interest, is one of the important tasks in information Extraction. There exist many methods for event detection, which usually treat event detection as a classification problem. These existing methods suffer from the imbalanced training data of different event types. To relieve this problem, this paper proposes a new Type-Representation-Enhanced Event Detection (TREED) method, which introduce hierarchical event type representations to enhance classification accuracy. Specifically, TREED first models the hierarchical relationship among event types in the dataset;Next, upon interactions between the representations of trigger words and event types, it obtains the new representations of trigger words integrating event type information. Finally, TREED classifies the events based on the obtained more accurate trigger word representations. Experimental results on a commonly used benchmark dataset, ACE-2005, demonstrate that the proposed model can improve both accuracy and recall of the event detection task. © 2023 IEEE.

关键词： Signal encoding

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Design and implementation of monitoring data storage and processing scheme based on distributed computing 3

Design and implementation of monitoring data storage and pro...

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3rd IEEE International Conference on Big data Analysis, ICBDA 2018

作者： Chi, Yaping Yang, Yintan Xu, Ping Li, Gefei Li, Shuhao Beijing Electronic Science and Technology Institute Key Laboratory of Network Assessment Technology Institute of Information Engineering CAS Beijing China Department of Communication Engineering Xidian University Beijing Electronic Science and Technology Institute Beijing China

ISBN: (纸本)9781538647936

The monitoring system in cloud computing environment will generate a large number of monitoring data in real time, how to achieve efficient storage and processing of monitoring data in the big data environment is particularly important. To solve this problem, a scheme of monitoring data storage and processing based on Hadoop is proposed. The scheme adopts the HBase database to store the timing monitoring data, and enhance the field method of wide table storage model improved HBase database to improve storage efficiency monitoring data;the traffic data, using MapReduce distributed computing to improve the processing efficiency. The experimental results show that the scheme is scientific and effective, and it can improve the processing speed of the monitoring system under the massive monitoring data, and solve the bottleneck problem of the monitoring data in the cloud computing environment. © 2018 IEEE.

关键词： Monitoring

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MINDFLOW: A network TRAFFIC ANOMALY DETECTION MODEL BASED ON MINDSPORE

arXiv

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arXiv 2025年

作者： Xiang, Qiuyan Wu, Shuang Wu, Dongze Liu, Yuxin Qin, Zhenkai School of Information Technology Guangxi Police College 530028 China College of Computer Science and AI Southwest Minzu University 610213 China Institute of Software Chinese Academy of Sciences 100190 China School of Computing and Information China Network Security Research Center China Big Data and Policing Technology Laboratory Guangxi Police College Guangxi Nanning China

With the wide application of IoT and industrial IoT technologies, the network structure is becoming more and more complex, and the traffic scale is growing rapidly, which makes the traditional security protection mechanism face serious challenges in dealing with high-frequency, diversified, and stealthy cyber-attacks. To address this problem, this study proposes MindFlow, a multi-dimensional dynamic traffic prediction and anomaly detection system combining convolutional neural network (CNN) and bi-directional long and short-term memory network (BiLSTM) architectures based on the MindSpore framework, and conducts systematic experiments on the NF-BoT-IoT dataset. The experimental results show that the proposed model achieves 99% in key metrics such as accuracy, precision, recall and F1 score, effectively verifying its accuracy and robustness in network intrusion detection. © 2025, CC0.

关键词： Convolutional neural networks

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Querying a matrix through matrix-vector products 46

Querying a matrix through matrix-vector products

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46th International Colloquium on Automata, Languages, and Programming, ICALP 2019

作者： Sun, Xiaoming Woodruff, David P. Yang, Guang Zhang, Jialin CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Carnegie Mellon University PittsburghPA United States Institute of Computing Technology Chinese Academy of Sciences Beijing China Conflux Beijing China

ISBN: (纸本)9783959771092

We consider algorithms with access to an unknown matrix M ∈ Fn×d via matrix-vector products, namely, the algorithm chooses vectors v1,..., vq, and observes Mv1,..., Mvq. Here the vi can be randomized as well as chosen adaptively as a function of Mv1,..., Mvi−1. Motivated by applications of sketching in distributed computation, linear algebra, and streaming models, as well as connections to areas such as communication complexity and property testing, we initiate the study of the number q of queries needed to solve various fundamental problems. We study problems in three broad categories, including linear algebra, statistics problems, and graph problems. For example, we consider the number of queries required to approximate the rank, trace, maximum eigenvalue, and norms of a matrix M;to compute the AND/OR/Parity of each column or row of M, to decide whether there are identical columns or rows in M or whether M is symmetric, diagonal, or unitary;or to compute whether a graph defined by M is connected or triangle-free. We also show separations for algorithms that are allowed to obtain matrix-vector products only by querying vectors on the right, versus algorithms that can query vectors on both the left and the right. We also show separations depending on the underlying field the matrix-vector product occurs in. For graph problems, we show separations depending on the form of the matrix (bipartite adjacency versus signed edge-vertex incidence matrix) to represent the graph. Surprisingly, this fundamental model does not appear to have been studied on its own, and we believe a thorough investigation of problems in this model would be beneficial to a number of different application areas. © Xiaoming Sun, David P. Woodruff, Guang Yang, and Jialin Zhang;licensed under Creative Commons License CC-BY

关键词： Matrix algebra

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One Comment from One Perspective: An Effective Strategy for Enhancing Automatic Music Comment 28

One Comment from One Perspective: An Effective Strategy for ...

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28th International Conference on Computational Linguistics, COLING 2020

作者： Huo, Tengfei Liu, Zhiqiang Zhang, Jinchao Zhou, Jie CAS Key Lab of Network Data Science and Technology China Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences Beijing China Pattern Recognition Center WeChat AI Tencent Inc. China

ISBN: (纸本)9781952148279

The automatic generation of music comments is of great significance for increasing the popularity of music and the music platform’s activity. In human music comments, there exists high distinction and diverse perspectives for the same song. In other words, for a song, different comments stem from different musical perspectives. However, to date, this characteristic has not been considered well in research on automatic comment generation. The existing methods tend to generate common and meaningless comments. In this paper, we propose an effective multi-perspective strategy to enhance the diversity of the generated comments. The experiment results on two music comment datasets show that our proposed model can effectively generate a series of diverse music comments based on different perspectives, which outperforms state-of-the-art baselines by a substantial margin. © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Music

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HoloScope: Topology-and-Spike Aware Fraud Detection

arXiv

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arXiv 2017年

作者： Liu, Shenghua Hooi, Bryan Faloutsos, Christos CAS Key Laboratory of Network Data Science & Technology Institute of Computing Technology Chinese Academy of Sciences Carnegie Mellon University

As online fraudsters invest more resources, including purchasing large pools of fake user accounts and dedicated IPs, fraudulent attacks become less obvious and their detection becomes increasingly challenging. Existing approaches such as average degree maximization suffer from the bias of including more nodes than necessary, resulting in lower accuracy and increased need for manual verification. Hence, we propose HoloScope, which uses information from graph topology and temporal spikes to more accurately detect groups of fraudulent users. In terms of graph topology, we introduce "contrast suspiciousness," a dynamic weighting approach, which allows us to more accurately detect fraudulent blocks, particularly low-density blocks. In terms of temporal spikes, HoloScope takes into account the sudden bursts and drops of fraudsters’ attacking patterns. In addition, we provide theoretical bounds for how much this increases the time cost needed for fraudsters to conduct adversarial attacks. Additionally, from the perspective of ratings, HoloScope incorporates the deviation of rating scores in order to catch fraudsters more accurately. Moreover, HoloScope has a concise framework and sub-quadratic time complexity, making the algorithm reproducible and scalable. Extensive experiments showed that HoloScope achieved significant accuracy improvements on synthetic and real data, compared with state-of-the-art fraud detection methods. Copyright © 2017, The Authors. All rights reserved.

关键词： Drops

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Marked temporal dynamics modeling based on recurrent neural network

arXiv

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arXiv 2017年

作者： Wang, Yongqing Liu, Shenghua Shen, Huawei Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

We are now witnessing the increasing availability of event stream data, i.e., a sequence of events with each event typically being denoted by the time it occurs and its mark information (e.g., event type). A fundamental problem is to model and predict such kind of marked temporal dynamics, i.e., when the next event will take place and what its mark will be. Existing methods either predict only the mark or the time of the next event, or predict both of them, yet separately. Indeed, in marked temporal dynamics, the time and the mark of the next event are highly dependent on each other, requiring a method that could simultaneously predict both of them. To tackle this problem, in this paper, we propose to model marked temporal dynamics by using a mark-specific intensity function to explicitly capture the dependency between the mark and the time of the next event. Extensive experiments on two datasets demonstrate that the proposed method outperforms state-of-the-art methods at predicting marked temporal dynamics. Copyright © 2017, The Authors. All rights reserved.

关键词： Forecasting

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