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arXiv 2020年

作者： He, Jieyue Yang, Xinxing Gong, Zhuo Zamit, Lbrahim School of Computer Science and Engineering Key Lab of Computer Network & Information Integration MOE Southeast University Nanjing210018 China

Background: Drug repositioning is designed to discover new uses of known drugs, which is an important and efficient method of drug discovery. Researchers only use one certain type of Collab.rative Filtering (CF) models for drug repositioning currently, like the neighborhood based approaches which are good at mining the local information contained in few strong drug-disease associations, or the latent factor based models which are effectively capture the global information shared by a majority of drug-disease associations. Few researchers have combined these two types of CF models to derive a hybrid model with the advantages of both of them. Besides, the cold start problem has always been a major challenge in the field of computational drug repositioning, which restricts the inference ability of relevant models. Results: Inspired by the memory network, we propose the Hybrid Attentional Memory network (HAMN) model, a deep architecture combines two classes of CF model in a nonlinear manner. Firstly, the memory unit and the attention mechanism are combined to generate the neighborhood contribution representation to capture the local structure of few strong drug-disease associations. Then a variant version of the autoencoder is used to extract the latent factor of drugs and diseases to capture the overall information shared by a majority of drug-disease associations. In that process, ancillary information of drugs and diseases can help to alleviate the cold start problem. Finally, in the prediction stage, the neighborhood contribution representation is combined with the drug latent factor and disease latent factor to produce the predicted value. Comprehensive experimental results on two real data sets show that our proposed HAMN model is superior to other comparison models according to the AUC, AUPR and HR indicators. Conclusions: Through the performance on two real-world data sets, we believe that the proposed model can effectively alleviate the cold start problem in th

关键词： Collab.rative filtering

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Conflict-free transmission schedule based on network coding in wireless networks

Conflict-free transmission schedule based on network coding ...

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International Conference on Communications and Mobile Computing

作者： Wang, Jing Liu, Xiangyang Wang, Xinmei School of Information Engineering Chang'an University Xi'an China Computer Network Teaching Office Xi'an Communication College Xi'an China State Key Lab. of Integrated Service Networks Xidian University Xi'an China

ISBN: (纸本)9780769543574

In wireless networks, the cross-layer design of joint medium access control and network coding can avoid destructive interferences among concurrent transmissions. Wireless network codes in conjunction with conflict-free network realizations are designed by Sagduyu. Then, this paper proposed a new conflict-free transmission schedule based on network coding to achieve better network performance. We construct sub tree graphs by sub tree decomposition, and based on the achieved sub tree graphs, time slots are assigned from the first sub tree. Nodes of the latter sub trees can be chosen to combine with the front time slots according to whether there exist conflicts or not. Finally, network codes allocation method for network with two sources is presented, which adopts the theory of graph coloring. © 2011 IEEE.

关键词： Wireless networks

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ACDNet: Attention-guided Collab.rative Decision network for Effective Medication Recommendation

arXiv

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arXiv 2023年

作者： Mi, Jiacong Zu, Yi Wang, Zhuoyuan He, Jieyue School of Computer Science and Engineering Key Lab of Computer Network and Information Integration MOE Southeast University Jiangsu Nanjing210018 China

Medication recommendation using Electronic Health Records (EHR) is challenging due to complex medical data. Current approaches extract longitudinal information from patient EHR to personalize recommendations. However, existing models often lack sufficient patient representation and overlook the importance of considering the similarity between a patient’s medication records and specific medicines. Therefore, an Attention-guided Collab.rative Decision network (ACDNet) for medication recommendation is proposed in this paper. Specifically, ACDNet utilizes attention mechanism and Transformer to effectively capture patient health conditions and medication records by modeling their historical visits at both global and local levels. ACDNet also employs a collab.rative decision framework, utilizing the similarity between medication records and medicine representation to facilitate the recommendation process. The experimental results on two extensive medical datasets, MIMIC-III and MIMIC-IV, clearly demonstrate that ACDNet outperforms state-of-the-art models in terms of Jaccard, PR-AUC, and F1 score, reaffirming its superiority. Moreover, the ablation experiments provide solid evidence of the effectiveness of each module in ACDNet, validating their contribution to the overall performance. Furthermore, a detailed case study reinforces the effectiveness of ACDNet in medication recommendation based on EHR data, showcasing its practical value in real-world healthcare scenarios. © 2023, CC BY.

关键词： Data mining

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A novel community detection method based on discrete particle swarm optimization algorithms in complex networks

A novel community detection method based on discrete particl...

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Congress on Evolutionary Computation

作者： Cen Cao Qingjian Ni Yuqing Zhai School of Computer Science and Engineering Nanjing China Key Lab of Computer Network & Information Integration MOE P.R.China

ISBN: (纸本)9781479974931

The community structure is one of the most common and important attributes in complex networks. Community detection in complex networks has attracted much attention in recent years. As an effective evolutionary computation technique, particle swarm optimization (PSO) algorithm has become a candidate for many optimization applications. However, PSO algorithm was originally designed for continuous optimization. In this paper, an improved simple discrete particle swarm optimization (ISPSO) algorithm and a discrete particle swarm optimization with redefined operator (IDPSO-RO) algorithm are proposed in the discrete context of community detection problem. Furthermore, a community correcting strategy is used to optimize the results. The performance of the two algorithms is tested on three real networks with known community structures. The experiment results show that ISPSO and IDPSO-RO algorithms using community correcting strategy can detect community structures more efficiently without prior knowledge about the size of communities and the number of communities.

关键词： Particle swarm optimization Complex networks Sociology Statistics Algorithm design and analysis Optimization Image edge detection

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Image-Text Matching with Intra-Modal Knowledge

SSRN

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SSRN 2022年

作者： Liu, Haoliang Zhang, Yu School of Computer Science and Engineering The Key Lab of Computer Network and Information Integration Ministry of Education Southeast University Nanjing211189 China

In this paper, we study the problem of image-text matching. Image-text matching aims to train an model that learns to identify whether or not an (image, caption) input pair constitutes an aligned pair. To boost image-text matching, the soft lab.l is introduced by knowledge distillation. Its benefit is generally attributed to the privileged information on similarities among image-text pairs provided by the teacher model. However, training a stronger teacher is computationally heavy and inefficient. In this paper, we propose a novel parameter-free Soft lab.ls Generation(SLG) method for image-text matching which can produce soft lab.ls with privileged information without the need of training a heavy teacher model. We propose to use intra-modal knowledge to generate the soft lab.l for each image-text pair, which provides an alternative to knowledge distillation. Our method is especially applicable to cases where a stronger teacher model is not availab.e, or only limited computation resources are provided. Systematic experiments conducted on public benchmarks demonstrate that our SLG is empirically effective. Our SLG achieves about 2%-3% recall@1 boosting than the baseline. © 2022, The Authors. All rights reserved.

关键词： Personnel training

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Question-answering aspect classification with hierarchical attention network 17th

Question-answering aspect classification with hierarchical a...

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17th China National Conference on Computational Linguistics, CCL 2018 and 6th International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2018

作者： Wu, Hanqian Liu, Mumu Wang, Jingjing Xie, Jue Shen, Chenlin School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration of Ministry of Education Southeast University Nanjing China NLP Lab School of Computer Science and Technology Soochow University Suzhou China Southeast University-Monash University Joint Graduate School Suzhou China

ISBN: (纸本)9783030017156

In e-commerce websites, user-generated question-answering text pairs generally contain rich aspect information of products. In this paper, we address a new task, namely Question-answering (QA) aspect classification, which aims to automatically classify the aspect category of a given QA text pair. In particular, we build a high-quality annotated corpus with specifically designed annotation guidelines for QA aspect classification. On this basis, we propose a hierarchical attention network to address the specific challenges in this new task in three stages. Specifically, we firstly segment both question text and answer text into sentences, and then construct (sentence, sentence) units for each QA text pair. Second, we leverage a QA matching attention layer to encode these (sentence, sentence) units in order to capture the aspect matching information between the sentence inside question text and the sentence inside answer text. Finally, we leverage a self-matching attention layer to capture different importance degrees of different (sentence, sentence) units in each QA text pair. Experimental results demonstrate that our proposed hierarchical attention network outperforms some strong baselines for QA aspect classification. © Springer Nature Switzerland AG 2018.

关键词： Text processing

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Image-Text Retrieval with Intra-Modal Soft lab.ls

SSRN

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SSRN 2022年

作者： Liu, Haoliang Zhang, Yu School of Computer Science and Engineering The Key Lab of Computer Network and Information Integration Ministry of Education Southeast University Nanjing211189 China

Image-text retrieval seeks to find relevant images from a huge image corpus given a textual description, and vice versa. The critical challenge of this task is to well learn image-text alignments to measure the image-text similarity. Existing works normally lab.l the negative/positive image-text pair with a binary lab.l. They assume that a text is only relevant to the paired images and irrelevant to other images in the corpus. However, using the binary lab.ls during training may be sub-optimal for several reasons. First, a binary lab.l can not reveal the precise similarity between an image and a text. Second, the annotated lab.ls may contain noise. In this paper, we study the idea of training the model with the soft lab.l information in which each image-text pair is associated with a specific soft lab.l. Intuitively, if the textual description of an image shares a high textual similarity with a text, the image should also be highly relevant to the text. Therefore, we determine the soft lab.l between the cross-modal (image, text) pair by the similarity between the intra-modal (image caption, text) pair, which is termed as intra-modal soft lab.l (ISL). In the experiment, we evaluated our method on benchmark datasets, which demonstrate the effectiveness of the proposed method. © 2022, The Authors. All rights reserved.

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Improvement of MODIS 8-day LAI/FPAR product with temporal filters to generate high quality time-series product

Improvement of MODIS 8-day LAI/FPAR product with temporal fi...

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2009 Joint Urban Remote Sensing Event

作者： Huifang, Zhang Runhe, Shi Honglin, Zhong Peiqing, Qu Juan, Sun Wenpeng, Lin Su, Li Key Lab. of Geographic Information Science Ministry of Education East China Normal University Shanghai China Key Laboratory of Ecosystem Network Observation and Modeling IGSNRR CAS Beijing China Key Lab. of Urban Ecology and Environment Restoration Shanghai Normal University Shanghai China Computer College Beijing Technology and Business University Beijing China

ISBN: (纸本)9781424434619

Numerous studies have reported that the timeseries terrestrial parameters such as the Normalized Difference Vegetation Index (NDVI), Leaf Area of Index (LAI), Fraction of Absorbed Photosynthetic Active Radiation (FPAR), derived from NOAA/AVHRR, SPOT/VEGETATION, TERRA or AQUA/MODIS, have played significant roles in researching the global environment, terrestrial ecosystems and related ecological researches. However, the remotely sensed signals are interfered severely by atmospheric conditions especially clouds and such noises exists in the time-series products as well. Therefore, to obtain a high quality time-series of terrestrial parameter is a necessary step before further studies. At present several methods have been applied to reduce the noise to construct a fine time-series of NDVI, but few studies concerning the other key terrestrial parameters, such as LAI, FPAR etc. In this paper, after comparing general methods in literatures, we designed a new method based on the Savitzky-Golay filter, which was applied to improve the quality of MODIS 8-Day LAI/FPAR Product to generate timeseries of LAI and FPAR with high quality. Our validation results indicate that more smooth and realistic time-series curves of LAI/FPAR can be obtained by using this new method, which exemplify the dynamic change of forests, crop or plants and key input parameters in modeling the complex land surface processing. ©2009 IEEE.

关键词： Time series

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A Private Statistic Query Scheme for Encrypted Electronic Medical Record System *

A Private Statistic Query Scheme for Encrypted Electronic Me...

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International Conference on computer Supported Cooperative Work in Design

作者： Xianxian Li Xuemei Fu Feng Yu Zhenkui Shi Jie Li Junhao Yang Guangxi Key Lab of Multi-source Information Mining & Security Guilin China Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China

In this paper, we propose a scheme that supports statistic query and authorized access control on an Encrypted Electronic Medical Records Databases(EMDB). Different from other schemes, it is based on Differential-Privacy(DP), which can protect the privacy of patients. By deploying an improved Multi-Authority Attribute-Based Encryption(MA-ABE) scheme, all authorities can distribute their search capability to clients under different authorities without additional negotiations. To our best knowledge, there are few studies on statistical queries on encrypted data. In this work, we consider that support differentially-private statistical queries. To improve search efficiency, we leverage the Bloom Filter(BF) to judge whether the keywords queried by users exists. Finally, we use experiments to verify and evaluate the feasibility of our proposed scheme.

关键词： Access control Data privacy Privacy Histograms Databases Conferences Cryptography

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BEIR:A Blockchain-based Encrypted Image Retrieval Scheme

BEIR:A Blockchain-based Encrypted Image Retrieval Scheme

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International Conference on computer Supported Cooperative Work in Design

作者： Xianxian Li Jie Li Feng Yu Xuemei Fu JunHao Yang Yue Chen Guangxi Key Lab of Multi-source Information Mining & Security Guilin China Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China

ISBN: (纸本)9781728165974;9781728165981

Encrypted image retrieval may return incorrect or incomplete results due to threats from malicious cloud servers. Most of the existing solutions focus on the efficiency and accuracy of retrieval, lack of verification of the completeness of search results, to achieve the reliability of search results and the transparency of the search process, we explore characteristic such as the decentralization and tamper-proof of blockchain, proposed a blockchain-based encrypted image retrieval scheme. This scheme stores the encrypted index on the blockchain (Ethereum), through the blockchain consensus mechanism and the function of searching on the smart contract, ensures the integrity and correctness of search results, then outsources the corresponding encrypted images to the cloud server to reduce storage cost, and designs a double-layer index structure using the bag of visual word model and simhash in the process of image similarity index. Experiments show that the reliability, high retrieval efficiency, and precision of the scheme also have a good privacy protection effect.

关键词： Privacy Visualization Image retrieval Smart contracts Blockchain Search problems Encryption

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