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World Congress on Intelligent Control and Automation (WCICA)

作者： Liping Chen Junping Du Katsunori Shimohara Maoqiang Song Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer Science Beijing University of Posts and Telecommunications Beijing China Department of Information Systems Design Doshisha University Kyotanabe Kyoto Japan Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing University of Posts and Telecommunications Beijing China

This paper concentrates on the evaluation system of hot topics in the online news about the emergency events. First, the overall technology framework of the system was established and description was formulated on the key issues need to be resolved prior to the evaluation of the hot topics. Focusing on the vector representation of the news documents and clustering algorithm of the news topics, a modified TF-IDF text representation model was formulated and an improved selection method of initial topic clustering center was established. The evaluation model of the hot topics was then established to evaluate the heat value of the clustered topics based on extracting websites feature parameters, such as the time properties of the news reports, reporting properties, and user attention. Finally, the paper takes news of 2011 Japan Earthquake as data source for algorism and model evaluation. The results showed that the identification and evaluation system of hot topics in online news about the emergency events was valid, and the evaluation result of hot topics by the systems agreed with the expected results. It lays a foundation for the subsequent studying about the tracking and evolution of the hot topics.

关键词： Vectors Heating Feature extraction Media Educational institutions Earthquakes Time frequency analysis

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Exploiting Probabilistic Siamese Visual Tracking with a Conditional Variational Autoencoder

Exploiting Probabilistic Siamese Visual Tracking with a Cond...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Wenhui Huang Jason Gu Peiyong Duan Sujuan Hou Yuanjie Zheng School of Information Science and Engineering Shandong Normal University China Dalhousie University Halifax Canada Key Lab of Intelligent Computing & Information Security in Universities of Shandong Shandong Normal University China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Shandong Normal University China

Visual tracking is a fundamental capability for robots tasked with humans and environment interaction. However, state-of-the-art visual tracking methods are still prone to failures and are imprecise when applied to challenging stereos, and their results are generally confidence agonistic. These methods depend on an embedded deep learning model to provide deterministic features or regression maps. A deterministic output with low confidence can result in disastrous consequences and lacks evidence needed for subsequent operations. Moreover, training data ambiguities or noise in the observations (so-called data uncertainty) can also lead to inherent uncertainty. In this paper, we focus on exploiting probabilistic Siamese visual tracking with a conditional variational autoencoder (CVAE). First, we build a bridge between the Siamese architecture and the CVAE and propose a novel Bayesian visual tracking method. Second, the proposed method generates a complete probability distribution that enables the production of multiple plausible tracking outputs. Third, CVAE conditioned by ground truth data encodes a low-dimensional latent space and conducts noise-injection training to prevent overfitting. Our proposed tracking method outperformed the state-of-the-art trackers on the VOT2016, VOT2018 and TColor-128 datasets.

关键词： Bridges Training Visualization Uncertainty Training data Probabilistic logic Probability distribution

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A embedding model for text classification

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Expert Systems 2019年第6期36卷 e12460-e12460页

作者： Liu, Wenfeng Liu, Peiyu Yang, Yuzhen Yi, Jing Zhu, Zhenfang School of Information Science and Engineering Shandong Normal University Jinan China School of Computer Heze University Heze China Shandong Normal University Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China School of Computer Science and Technology Shandong Jianzhu University Jinan China School of Information Science and Electrical Engineering Shandong Jiaotong University Jinan China

Existing word embeddings learning algorithms only employ the contexts of words, but different text documents use words and their relevant parts of speech very differently. Based on the preceding assumption, in order to obtain appropriate word embeddings and further improve the effect of text classification, this paper studies in depth a representation of words combined with their parts of speech. First, using the parts of speech and context of words, a more expressive word embeddings can be obtained. Further, to improve the efficiency of look-up tables, we construct a two-dimensional table that is in the format to represent words in text documents. Finally, the two-dimensional table and a Bayesian theorem are used for text classification. Experimental results show that our model has achieved more desirable results on standard data sets. And it has more preferable versatility and portability than alternative models. © 2019 John Wiley & Sons, Ltd

关键词： Embeddings

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Multiple Feature Fusion-based Video Face Tracking for IoT Big Data

arXiv

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arXiv 2021年

作者： Li, Tianping Liu, Zhifeng Qiao, Jianping Key Laboratory of Medical Physics and Image Processing in Shandong Province Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Shandong Normal University School of Physics and Electronics Jinan Shandong China School of Physics and Electronics Shandong Normal University JinanShandong China School of Physics and Electronics Shandong Normal University Jinan China

With the advancement of IoT and artificial intelligence technologies, and the need for rapid application growth in fields such as security entrance control and financial business trade, facial information processing has become an important means for achieving identity authentication and information security. In this paper, we propose a multi-feature fusion algorithm based on integral histograms and a real-time update tracking particle filtering module. First, edge and colour features are extracted, weighting methods are used to weight the colour histogram and edge features to describe facial features, and fusion of colour and edge features is made adaptive by using fusion coefficients to improve face tracking reliability. Then, the integral histogram is integrated into the particle filtering algorithm to simplify the calculation steps of complex particles. Finally, the tracking window size is adjusted in real time according to the change in the average distance from the particle centre to the edge of the current model and the initial model to reduce the drift problem and achieve stable tracking with significant changes in the target dimension. The results show that the algorithm improves video tracking accuracy, simplifies particle operation complexity, improves the speed, and has good anti-interference ability and robustness. © 2021, CC BY.

关键词： Monte Carlo methods

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FCloudless: A Performance-Aware Collaborative Mechanism for JointCloud Serverless

FCloudless: A Performance-Aware Collaborative Mechanism for ...

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IEEE International Conference on Joint Cloud Computing (JCC)

作者： Jianfei Liu Huaimin Wang Peichang Shi Yaojie Li Penghui Ma Guodong Yi National Key Laboratory of Parallel and Distributed Computing College of Computer Science National University of Defense Technology Changsha 410073 China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha 410073 China Xiangjiang Lab Changsha 410073 China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha 410073 China

As a new stage in the development of the cloud computing paradigm, serverless computing has the high-level abstraction characteristic of shielding underlying details. This makes it extremely challenging for users to choose a suitable serverless platform. To address this, targeting the jointcloud computing scenario of heterogeneous serverless platforms across multiple clouds, this paper presents a jointcloud collaborative mechanism called FCloudless with cross-cloud detection of the full lifecycle performance of serverless platforms. Based on the benchmark metrics set that probe performance critical stages of the full lifecycle, this paper proposes a performance optimization algorithm based on detected performance data that takes into account all key stages that affect the performance during the lifecycle of a function and predicts the overall performance by combining the scores of local stages and dynamic weights. We evaluate FCloudless on AWS, AliYun, and Azure. The experimental results show that FCloudless can detect the underlying performance of serverless platforms hidden in the black box and its optimization algorithm can select the optimal scheduling strategy for various applications in a jointcloud environment. FCloudless reduces the runtime by 23.3% and 24.7% for cold and warm invocations respectively under cost constraints.

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UCL-AST: Active Self-Training with Uncertainty-Aware Clouded Logits for Few-Shot Text Classification

UCL-AST: Active Self-Training with Uncertainty-Aware Clouded...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Yi Xu Jie Hu Zhiqiao Gao Jinpeng Chen School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing China China Telecom Corporation Limited Research Institute Beijing China

Although the recent advances in pre-training language models have achieved great success and migrated annotation bottleneck for many tasks, the task-specific fine-tuning for text classification still requires thousands of labeled samples or even more. When few human annotations are available, active self-training is one of the efficient approaches for reducing human annotation costs which integrates pseudo labels of self-training into the procedure of active learning. The traditional active self-training approaches mix manual labels and pseudo labels indistinguishably without considering the impact of active learning and self-training on each other. There are two problems for active self-training in few-shot text classification: (1) The noise in a large number of pseudo-labels may mislead the model training. (2) The confusing samples in overlap boundaries between classes are especially difficult to identify correctly with a few labels. To solve these problems, we propose UCL-AST, an active self-training framework that boosts the performance of the active teacher model to provide superior pseudo labels for the self-training student model and facilitates the active teacher model to learn more clear class boundaries with uncertainty-aware clouded logits in few-shot. The extensive experiments and visualization analysis have demonstrated the effectiveness of the proposed framework on four datasets compared with state-of-the-art methods.

关键词： Training Visualization Uncertainty Costs Annotations Text categorization Measurement uncertainty

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EFM: A Negative Network Public Opinion Early Warning Model Based on Evolutionary Feature Mining

EFM: A Negative Network Public Opinion Early Warning Model B...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Fanfeixue Meng Jingchen Wu Feifan Song Xu Wu Lan Hu Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Cyberspace Security BUPT Beijing University of Posts and Telecommunications Beijing China School of Computer Science (National Pilot Software Engineering School) BUPT School of Economics and Finance Changchun Finance College

With the popularity of the Internet and the increase in the number of netizens, it is of great significance to effectively monitor and predict the negative network public opinion on social media platforms. Most mainstream negative network public opinion early warning models mainly focus on mining textual features, while neglecting the response and dissemination among netizens during the process of negative network public opinion fermentation. This is one of the important reasons for the accuracy bottleneck of them. This article proposes a negative network public opinion early warning model based on evolutionary feature mining. Firstly, by using entity linking techniques based on knowledge graphs, domain prior knowledge is supplemented for the text need to be analyzed for negative network public opinion. Then, the text sentiment features extracted based on long short-term memory networks and the evolutionary features extracted based on graph convolutional networks are fused through an enhanced-feature fusion method. Finally, the features are used for classification and early warning. Experimental results based on the Weibo public dataset show that the EFM model has higher early warning performance for negative network public opinion compared to other baseline models. The accuracy of this model is 0.959.

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Predicting employee relations in enterprises by bi-directional link mining

Predicting employee relations in enterprises by bi-direction...

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World Congress on Intelligent Control and Automation (WCICA)

作者： Wang Hong Sun Yanshen Institute of Information Science and Engineering Shandong Normal University Jinan Shandong Province China Computer Software Novel Technology Shandong Provincial Key Laboratory for Distributed Jinan Shandong Province China Department of Earth Sciences ZheJiang University Hangzhou Zhejiang Province China

By using link mining method, we can understand associations among persons in enterprises. In this paper, we propose a link mining model called a bi-directional link mining, which is capable of predicting edges that do not exist but may appear in the future in dynamic multi-dimensional networks. Firstly, we construct a dynamic multi-dimensional networks from an E-mail network and a telephone network, which makes us correctly describe a Group's staff relationship. Then we present some algorithms to build a reduction dimensional network and a dynamic network. After that, we give bi-directional link mining methods in a dynamic multi-dimensional weighted network and apply these methods in the enterprise's employee relational network. Experimental results show that these methods can effectively predict associations among persons in a large scale employee networks.

关键词： Electronic mail Heuristic algorithms Personnel Bidirectional control Prediction algorithms Data mining Social network services

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Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need 38

Unlearnable 3D Point Clouds: Class-wise Transformation Is Al...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wang, Xianlong Li, Minghui Liu, Wei Zhang, Hangtao Hu, Shengshan Zhang, Yechao Zhou, Ziqi Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China

Traditional unlearnable strategies have been proposed to prevent unauthorized users from training on the 2D image data. With more 3D point cloud data containing sensitivity information, unauthorized usage of this new type data has also become a serious concern. To address this, we propose the first integral unlearnable framework for 3D point clouds including two processes: (i) we propose an unlearnable data protection scheme, involving a class-wise setting established by a category-adaptive allocation strategy and multi-transformations assigned to samples;(ii) we propose a data restoration scheme that utilizes class-wise inverse matrix transformation, thus enabling authorized-only training for unlearnable data. This restoration process is a practical issue overlooked in most existing unlearnable literature, i.e., even authorized users struggle to gain knowledge from 3D unlearnable data. Both theoretical and empirical results (including 6 datasets, 16 models, and 2 tasks) demonstrate the effectiveness of our proposed unlearnable framework. Our code is available at https://***/CGCL-codes/UnlearnablePC. © 2024 Neural information processing systems foundation. All rights reserved.

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An attention-based syntax-tree and tree-LSTM model for sentence summarization

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International Journal of Performability Engineering 2017年第5期13卷 775-782页

作者： Liu, Wenfeng Liu, Peiyu Yang, Yuzhen Gao, Yaling Yi, Jing School of Information Science and Engineering Shandong Normal University Jinan250014 China Department of Computer and Information Engineering Heze University Heze274015 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250014 China Ruipu Peony Industrial Technology Development Co. Ltd 274015 China School of Computer Science and Technology Shandong Jianzhu University Jinan250101 China

Generative Summarization is of great importance in understanding large-scale textual data. In this work, we propose an attention-based Tree-LSTM model for sentence summarization, which utilizes an attention-based syntactic structure as auxiliary information. Thereinto, block-alignment is used to align the input and output syntax blocks, while inter-alignment is used for alignment of words within that of block pairs. To some extent, block-alignment can prevent structural deviations on the long sentences and inter-alignment is capable of increasing the flexibility of the generation in the blocks. This model can be easily trained to end-to-end mode and deal with any length of the input sentences. Compared with several relatively strong baselines, our model has achieved the state-of-art on DUC-2004 shared task. © 2017 Totem Publisher, Inc. All rights reserved.

关键词： Alignment

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