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International Conference on Advanced Cloud and Big Data (CBD)

作者： Feng Yu Xixian Li Huimin Zhang Li-e Wang College of computer science Guangxi Normal University Guilin China Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China Guangxi Key Lab of Multi-source Information Mining &Security Guilin China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China

Despite the importance of the secure data propagation in the edge-cloud orchestrated computing paradigms, to the best of our knowledge, there is lack of systematic and comprehensive review on this research topic. The purpose of this article is to provide a systematic literature survey on secure data propagation in edge-cloud orchestrated computing environment to examine the current technical achievements published between 2010 and February 2020. The brief contributions of the paper are as follows: (1)providing a comprehensive literature review of the secure data propagation approaches in the edge-cloud orchestrated computing paradigms, (2) designing a methodology for document retrieval and selection, (3) presenting analysis and comparison for main technique methods of secure data propagation in hybrid edge-cloud environment.

关键词： Systematics Bibliographies Taxonomy Big Data

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A Control Task Assignment Algorithm based on sub-graph isomorphism

A Control Task Assignment Algorithm based on sub-graph isomo...

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International Conference on Advanced Cloud and Big Data (CBD)

作者： Feng Yu Xixian Li Huimin Zhang Li-e Wang College of computer science Guangxi Normal University Guilin China Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China Guangxi Key Lab of Multi-source Information Mining & Security Guilin China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China

To enhance the controllability and trustworthiness of the Internet, a new network control architecture has been presented with control task extracted to the intra-domain control node and executed in distributed multi-domain cooperative style. The study on control task assigning approach plays a central role in this network control pattern and shares some features with virtual network mapping problem. Different from the previous heuristics-based algorithms, we designs a baseline assigning algorithm which tests if the control task request can be mapped to a sub-graph of substrate control network or not without control task migration. Subsequently, an extend version is described under task migration considered. Our experiment evaluations show the impact of parameters on the runtime and assigning accuracy rate.

关键词： Cloud computing Backtracking Runtime Heuristic algorithms Process control Big Data Feature extraction

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Triple-view Learning for Predicting Antibiotic Resistance Genes

Triple-view Learning for Predicting Antibiotic Resistance Ge...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shujie Luo Haifang Wu Weizhong Zhao Xingpeng Jiang Tingting He Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Central China Normal University Wuhan PR China School of Computer Central China Normal University Wuhan PR China National Language Resources Monitoring & Research Center for Network Media Central China Normal University Wuhan PR China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin PR China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin PR China

ISBN: (纸本)9781665429825

With the increasing resistance of bacteria to antibiotics, the problem of antibiotic resistance has become a major challenge in healthcare. Therefore, the accurate identification of antibiotic resistance genes (ARGs) in bacteria is particularly urgent and important. Although culture-based methods can accurately identify ARGs, they are limited by domain knowledge and are time-consuming. With the availability of whole genome sequence data, most existing methods rely on sequence alignment to calculate sequence similarity to predict ARGs. However, for ARGs that has low sequence identity with known sequences, the existing methods do not identify them well. To address the challenges, we propose a multiple view learning based framework for ARGs. Experimental results demonstrate that the proposed approach performs better than the selected representative baselines on the real-world dataset.

关键词： Deep learning Microorganisms Antibiotics Semantics Genomics Medical services Predictive models

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An improved RL-based framework for multiple biomedical event extraction via self-supervised learning

An improved RL-based framework for multiple biomedical event...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yao Zhao Weizhong Zhao Xingpeng Jiang Tingting He Bianping Su Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Central China Normal University Wuhan Hubei PR China School of Computer Central China Normal University Wuhan Hubei PR China National Language Resources Monitoring & Research Center for Network Media Central China Normal University Wuhan Hubei PR China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin PR China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin PR China Xi’an University of Architecture and Technology Xi’an Shanxi PR China

ISBN: (纸本)9781665429825

The main goal of biomedical event extraction is to structurally extract biomedical events from texts, however, the specificity of the domain makes both text modeling and data annotation very difficult. We propose a self-supervised learning-based data augmentation method in this paper and design specific augmentation strategies for biomedical entities and event triggers in biomedical texts, which solves the problem of sparse annotation data to some extent. In addition we improve the reinforcement learning-based event extraction method to improve the training efficiency of the model. The experiments on two datasets demonstrate the effectiveness of our method.

关键词： Training Annotations Conferences Biological system modeling Data models Data mining Bioinformatics

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DONet: Dual Objective networks for skin lesion segmentation

arXiv

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arXiv 2020年

作者： Wang, Yaxiong Wei, Yunchao Qian, Xueming Zhu, Li Yang, Yi School of Software Engineering Xi’an Jiaotong University Xi’an710049 China ReLER Lab University of Technology Sydney Australia Centre for Artificial Intelligence University of Technology Sydney UltimoNSW2007 Australia Key Laboratory for Intelligent Networks and Network Security Ministry of Education XiâĂŹan Jiaotong University XiâĂŹan 710049 China SMILES Laboratory XiâAZan Jiaotong University XiâAZan710049 China Zhibian Technology Co. Ltd. Taizhou317000 China School of Software XiâĂŹan Jiaotong University XiâĂŹan 710049 China

Skin lesion segmentation is a crucial step in the computer-aided diagnosis of dermoscopic images. In the last few years, deep learning based semantic segmentation methods have significantly advanced the skin lesion segmentation results. However, the current performance is still unsatisfactory due to some challenging factors such as large variety of lesion scale and ambiguous difference between lesion region and background. In this paper, we propose a simple yet effective framework, named Dual Objective networks (DONet), to improve the skin lesion segmentation. Our DONet adopts two symmetric decoders to produce different predictions for approaching different objectives. Concretely, the two objectives are actually defined by different loss functions. In this way, the two decoders are encouraged to produce differentiated probability maps to match different optimization targets, resulting in complementary predictions accordingly. The complementary information learned by these two objectives are further aggregated together to make the final prediction, by which the uncertainty existing in segmentation maps can be significantly alleviated. Besides, to address the challenge of large variety of lesion scales and shapes in dermoscopic images, we additionally propose a recurrent context encoding module (RCEM) to model the complex correlation among skin lesions, where the features with different scale contexts are efficiently integrated to form a more robust representation. Extensive experiments on two popular benchmarks well demonstrate the effectiveness of the proposed DONet. In particular, our DONet achieves 0.881 and 0.931 dice score on ISIC 2018 and PH2, respectively. Code will be made public available. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

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Optimizing network resource allocation with graph pointer neural network in large-scale AI systems

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Digital Communications and networks 2024年

作者： Peiying Zhang Yilin Li Athanasios V. Vasilakos Konstantin Igorevich Kostromitin Jianyong Zhang Mohsen Guizani Qingdao Institute of Software College of Computer Science and Technology China University of Petroleum (East China) Qingdao 266580 China State Key Laboratory of Integrated Services Networks Xidian University Xi'an 710071 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan 250013 China Department of Networks and Communications College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam 31441 Saudi Arabia Department of Physics of Nanoscale Systems South Ural State University Chelyabinsk 454080 the Russian Federation Institute of Lightwave Technology Beijing Jiaotong University Beijing 100044 China Key Lab of All Optical Network and Advanced Telecommunication of EMC Institute of Lightwave Technology Beijing Jiaotong University Beijing 100044 China Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi 999041 United Arab Emirates

For increasingly complex communication demands of large-scale AI communication systems, the Space-Air-Ground Integrated network (SAGIN) better caters to demands but also raises concerns about resource scarcity and diversity. This paper innovatively combines Graph Pointer Neural networks (GPNN) and Reinforcement Learning (RL) to enhance resource allocation efficiency. The method leverages the advantages of GPNN in handling graph data and RL in optimizing decisions in dynamic environments. It also targets the optimization goal of maximizing resource allocation while minimizing deployment latency. This paper begins by modeling SAGIN and elucidating the SAGIN logical architecture based on software-defined networking (SDN). Subsequently, it introduces an SFC deployment algorithm aimed at joint optimization of resource allocation and latency. The algorithm leverages GPNN and RL to deploy virtual nodes and links, with the goal of optimizing resource allocation and deployment latency. Experiment findings conclusively demonstrate that the efficacy of proposed algorithm in effectively weighing limited heterogeneous resources and minimum mapping delay. Notably, when compared to three other SFC mapping algorithms MLRL, NFVdeep, and RL, the proposed algorithm consistently outperforms them, with an average improvement of 10.17% in long-term average reward/cost, 11.21% in link resource utilization ratio, 15.34% in node resource utilization ratio, and 16.38% in acceptance ratio.

关键词： Space-air-ground integrated network Topological structure Reinforcement learning Service function chain Graph pointer neural network

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Knowledge-aware Few-shot Learning Framework for Biomedical Event Trigger Identification

Knowledge-aware Few-shot Learning Framework for Biomedical E...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shujuan Yin Weizhong Zhao Xingpeng Jiang Tingting He Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Central China Normal University Wuhan Hubei PR China School of Computer Central China Normal University Wuhan Hubei PR China National Language Resources Monitoring & Research Center for Network Media Central China Normal University Wuhan Hubei PR China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Biomedical event extraction aims to detect fine-grained interactions between biomedical entities in biomedical texts, and has become a research hotspot for researchers in the biomedical field. As the first step in biomedical event extraction, biomedical event trigger identification plays an important role in the whole process. Although researchers have proposed many methods, the performance of existing methods is not desirable due to the reliance on a large number of labeled training samples and the need for expert knowledge in the biomedical field. To treat this issue, we model the biomedical event trigger identification as a few-shot learning problem. Specifically, we utilize a knowledge-aware attention layer to obtain a rich informative representation for entities, and improve the derived prototypes accordingly by prototypical network. In addition, the module of relation network is introduced to train a more reasonable distance function for trigger type prediction. The results demonstrate the effectiveness of the proposed framework according to F1-score.

关键词： Training Task analysis Biological system modeling Testing Prototypes Feature extraction Annotations

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An Effective Framework for Document-level Chemical-induced Disease Relation Extraction via Fine-grained Interaction between Contexts

An Effective Framework for Document-level Chemical-induced D...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jinyong Zhang Weizhong Zhao Jincai Yang Xingpeng Jiang Tingting He Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Central China Normal University Wuhan Hubei PR China School of Computer Central China Normal University Wuhan Hubei PR China National Language Resources Monitoring & Research Center for Network Media Central China Normal University Wuhan Hubei PR China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

In recent years, Chemical-induced Disease (CID) relations are the most searched topics by PubMed users worldwide, reflecting its extensive applications in biomedical research and public health field. However, for CID relation extraction, prior methods fail to make full use of the interaction between local and global contexts in biomedical document. To better capture the complex relationships among contexts, we propose an effective framework for document-level CID relation extraction. Specifically, the stacked Hypergraph Aggregation Neural network (HANN) layers are applied to model effectively the interaction between local and global contexts. Moreover, by constructing CID Relation Heterogeneous Graph, we can capture the different granularities of information and learn better contextualized representations for CID relation extraction. Extensive experiments on a commonly used dataset demonstrate the effectiveness of the proposed method.

关键词： Chemicals Diseases Data mining Context modeling Biomedical optical imaging Biological system modeling Drugs

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Few-example object detection with model communication

arXiv

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arXiv 2017年

作者： Dong, Xuanyi Zheng, Liang Ma, Fan Yang, Yi Meng, Deyu Centre for Artificial Intelligence University of Technology SydneyNSW Australia State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi'an Jiaotong University Shaanxi China

In this paper, we study object detection using a large pool of unlabeled images and only a few labeled images per category, named "few-example object detection". The key challenge consists in generating trustworthy training samples as many as possible from the pool. Using few training examples as seeds, our method iterates between model training and high-confidence sample selection. In training, easy samples are generated first and, then the poorly initialized model undergoes improvement. As the model becomes more discriminative, challenging but reliable samples are selected. After that, another round of model improvement takes place. To further improve the precision and recall of the generated training samples, we embed multiple detection models in our framework, which has proven to outperform the single model baseline and the model ensemble method. Experiments on PASCAL VOC'07, MS COCO'14, and ILSVRC'13 indicate that by using as few as three or four samples selected for each category, our method produces very competitive results when compared to the state-of-the-art weakly-supervised approaches using a large number of image-level labels. Copyright © 2017, The Authors. All rights reserved.

关键词： Object detection

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Active self-paced learning for cost-effective and progressive face identification

arXiv

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arXiv 2017年

作者： Lin, Liang Wang, Keze Meng, Deyu Zuo, Wangmeng Zhang, Lei School of Data and Computer Science Sun Yat-sen University Guangzhou China Engineering Research Center Advanced Computing Engineering Software of Ministry of Education China School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi'an Jiaotong University China School of Computer Science and Technology Harbin Institute of Technology Harbin China Dept. of Computing Hong Kong Polytechnic University Hong Kong

This paper aims to develop a novel cost-effective framework for face identification, which progressively maintains a batch of classifiers with the increasing face images of different individuals. By naturally combining two recently rising techniques: active learning (AL) and self-paced learning (SPL), our framework is capable of automatically annotating new instances and incorporating them into training under weak expert recertification. We first initialize the classifier using a few annotated samples for each individual, and extract image features using the convolutional neural nets. Then, a number of candidates are selected from the unannotated samples for classifier updating, in which we apply the current classifiers ranking the samples by the prediction confidence. In particular, our approach utilizes the high-confidence and low-confidence samples in the self-paced and the active user-query way, respectively. The neural nets are later fine-tuned based on the updated classifiers. Such heuristic implementation is formulated as solving a concise active SPL optimization problem, which also advances the SPL development by supplementing a rational dynamic curriculum constraint. The new model finely accords with the "instructor-student-collaborative" learning mode in human education. The advantages of this proposed framework are two-folds: i) The required number of annotated samples is significantly decreased while the comparable performance is guaranteed. A dramatic reduction of user effort is also achieved over other state-of-the-art active learning techniques. ii) The mixture of SPL and AL effectively improves not only the classifier accuracy compared to existing AL/SPL methods but also the robustness against noisy data. We evaluate our framework on two challenging datasets, which include hundreds of persons under diverse conditions, and demonstrate very promising results. Please find the code of this project at: http://***/projects/aspl/ Copyright © 201

关键词： Cost effectiveness

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