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Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2021

作者： Bai, Yu Gao, Yang Huang, Heyan School of Computer Science and Technology Beijing Institute of Technology Beijing China Southeast Academy of Information Technology Fujian China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China

ISBN: (纸本)9781954085527

Parallel cross-lingual summarization data is scarce, requiring models to better use the limited available cross-lingual resources. Existing methods to do so often adopt sequence-to-sequence networks with multi-task frameworks. Such approaches apply multiple decoders, each of which is utilized for a specific task. However, these independent decoders share no parameters, hence fail to capture the relationships between the discrete phrases of summaries in different languages, breaking the connections in order to transfer the knowledge of the high-resource languages to low-resource languages. To bridge these connections, we propose a novel Multi-Task framework for Cross-Lingual Abstractive Summarization (MCLAS) in a low-resource setting. Employing one unified decoder to generate the sequential concatenation of monolingual and cross-lingual summaries, MCLAS makes the monolingual summarization task a prerequisite of the cross-lingual summarization (CLS) task. In this way, the shared decoder learns interactions involving alignments and summary patterns across languages, which encourages attaining knowledge transfer. Experiments on two CLS datasets demonstrate that our model significantly outperforms three baseline models in both low-resource and full-dataset scenarios. Moreover, in-depth analysis on the generated summaries and attention heads verifies that interactions are learned well using MCLAS, which benefits the CLS task under limited parallel resources. © 2021 Association for Computational Linguistics

关键词： Decoding

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2-D Molecular Descriptor Screening and Quantitative Structure-Activity Relationship Modeling for Estrogen Receptors Alpha Bioactivity

2-D Molecular Descriptor Screening and Quantitative Structur...

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作者： Shiyu Liu Kangli Chang Hao Yan Dongfang Li Fuchuan Li Beijing Institute of Computer Technology and Applications

With the continuous development of data mining in the field of computational biology and bioinformatics in recent years,it is possible to develop better quantitative prediction models based on these *** structureactivity relationship(QSAR) of compounds can be modeled using molecular descriptors and bioactivity data of compounds,so as to predict new compound molecules with better bioactivity,or guide the structural optimization of existing active *** the molecular descriptors of compounds,2-D descriptors can be easily obtained through COTS toolbox,so as to avoid some complex descriptor calculation ***,2-D descriptors also have the problems of over fitting and poor model *** this paper,the recursive feature elimination based on random forest(RFE-RF) is used to sort 2-D molecular *** each round of recursion,RF is used to eliminate the features,and the molecular descriptors that have the greatest impact on bioactivity are selected round by ***-RF significantly reduces the types of descriptors,not only improves the calculation speed of the prediction model,but also effectively avoids the common over fitting problem in computational *** selected molecular descriptors are used to model,train and verify RF *** regression model can predict the bioactivity of compounds against Estrogen receptors alpha,and Estrogen receptors alpha is an important target for breast cancer *** this paper,the predicted data and experimental data are compared and analyzed,and the correctness and applicability of the modeling are evaluated from the aspects of variance,standard deviation,mean absolute error and root mean square error.

关键词： Random forest(RF) Quantitative structure activity relationship(QSAR) Candidate drug optimization modeling Recursive feature elimination(RFE)

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Image Hashing Based on SIFT Features

Image Hashing Based on SIFT Features

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作者： Pingyuan Li Xiaoguang Yuan Suiping Jiang Beijing Institute of Computer Technology and Applications

Image hashing maps high-dimensional image to low-dimensional binary code which can be used for fast image retrieval. A new image hashing method is proposed in this paper. Firstly, the SIFT features of an image are extracted. Secondly, the SIFT feature's 128 dimensional components are divided into 16 groups and the differential SIFT feature of each group is calculated in a sequential and cyclic way. Thirdly, the weight of each SIFT feature is calculated and is applied to the corresponding differential SIFT feature. Finally, the weighted differential SIFT features are summed to get the image's SIFT feature, and binarization is carried out on each group of the image's SIFT feature in a sequential and cyclic way to obtain the hash code of the image. The proposed method has the advantage that it can quickly generate hash codes for large-scale data sets and is robust to rotation and noise. The experimental results on caltech-256 showed that the proposed method can improve the effect of image retrieval.

关键词： Image retrieval Image hashing SIFT feature

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Automatic Road Extraction Using Particle Filters from High Resolution Images

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Journal of China University of Mining and technology 2006年第4期16卷 490-493页

作者： YE Fa-mao SU Lin TANG Jiang-long Department of Computer Science and Engineering Nanchang University Nanchang Jiangxi 330029 China Institute of Remote Sensing Applications Chinese Academy of Sciences Beijing 100101 China East China Institute of Technology Fuzhou Jiangxi 344000 China

Road extraction plays an important role in many applications such as car navigation, but the manual extraction of roads is a laborious, tedious task. To speed the extraction of roads, an approach based on particle filtering to extract automatically roads from high resolution imagery is proposed. Particle filtering provides a statistical framework for propagating sample-based approximations of posterior distributions and has almost no restriction on the ingredients of the model. We integrate the similarity of grey value and the edge point distribution of roads into particle filtering to deal with complex scenes. To handle road appearance changes the tracking algorithm is allowed to update the road model during temporally stable image observations. A fully automatic initialization strategy is used. Experimental results show that the proposed approach is a promising and fully automatic method for extracting roads from images, even in the presence of occlusions.

关键词： road extraction particle filters high resolution images

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Model Checking for an HDLC IP Core Transparency Error in an Aerospace Industrial Context

Model Checking for an HDLC IP Core Transparency Error in an ...

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作者： Shiyu Liu Dongfang Li Jixing Xue Guang Yang Wei Shen Zhihao Wang Beijing Institute of Computer Technology and Applications

In recent years, the use of field-specific and customizable FPGA devices in the aerospace industry has become a standard practice because FPGA devices offer new trade-offs between flexibility, energy,and performance compared with general-purpose processors. The development of FPGA devices relying on IP core reuse technology has become the norm. IP cores that are reused are often regarded as safer, which makes the verification team neglect to verify. But in fact, limited by the level of verification technology during development, IP cores that have been reused for a long time may have some boundary errors that are difficult to find in simulation *** this article, we apply the model check to a commercial HDLC controller, which has been repeatedly used for nearly ten years on multiple spacecraft undertaking research by related aerospace industry departments. The model check found a logic error in the sending function of the IP core, and analyzed the timing path that triggered the error. This work demonstrates the efficiency of model checking in dealing with such boundary case errors.

关键词： FPGA IP core model checking formal verification

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A chunk-based multi-strategy machine translation method 5th

A chunk-based multi-strategy machine translation method

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5th Euro-China Conference on Intelligent Data Analysis and applications, ECC 2018

作者： Wang, Yiou Zhang, Fuquan Beijing Institute of Science and Technology Information Beijing100044 China Digital Performance and Simulation Technology Lab School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China

ISBN: (纸本)9783030037659

In this paper, a chunk-based multi-strategy machine translation method is proposed. Firstly, an English-Chinese bilingual tree-bank is constructed. Then, a translation strategy based on the chunk that combines statistics and rules is used in the translation stage. Through hierarchical sub-chunks, the input sentence is divided into a set of chunk sequence. Each chunk searches the corresponding instance in the corpus. Translation is completed by recursive refinement from chunks to words. Conditional Random Fields model is used to divide chunks. An experimental English-Chinese translation system is deployed, and experimental results show that the system performs better than the Systran system. © Springer Nature Switzerland AG 2019.

关键词： Machine translation

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Quantitative Structure-Activity Relationship Modeling of Estrogen Receptor Alpha Bioactivity based on Multiple Algorithms

Quantitative Structure-Activity Relationship Modeling of Est...

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2021年第四届算法、计算和人工智能国际会议

作者： Kangli Chang Shiyu Liu Hao Yan Fuchuan Li Dongfang Li Beijing Institute of Computer Technology and Applications

Currently, bioactivity analysis and ADMET property analysis of compound molecules in drug discovery are time-consuming and labor-intensive, so the approach of building compound bioactivity prediction models is usually used to screen potentially active compounds, especially the rapid development of data mining and machine learning methods has greatly facilitated this process. The aim of this paper is to construct quantitative prediction models for the bioactivity of compounds ERα. Firstly, 12 molecular descriptors that are strongly correlated with bioactivity and independent of each other were screened from 729 molecular descriptors to construct a quantitative prediction model of compound ERα bioactivity based on BP neural network, decision tree and random forest,respectively. Finally, the three models were evaluated with four evaluation metrics-variance, standard deviation, mean absolute error, and root mean square error. According to the comparison results, the best prediction model is the random forest model, which can be used to predict new compound molecules with better ERαbioactivity in the future.

关键词： 2D molecular descriptors QSAR quantitative prediction model for ERα bioactivity of compounds BP neural network decision tree random forest

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CrowdX: Enhancing automatic construction of indoor floorplan with opportunistic encounters

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Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018年第4期2卷 1–21页

作者： Chen, Huijie Li, Fan Hei, Xiaojun Wang, Yu School of Computer Science Beijing Institute of Technology Beijing Engineering Research Center High Volume Language Information Processing and Cloud Computing Applications Beijing China School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China Department of Computer Science University of North Carolina at Charlotte Charlotte United States

The lack of floorplan limits the spread of pervasive indoor location-based services. Existing crowdsourcing based approaches mostly rely on identifying, locating landmarks in the environment and utilizing the spatial relationship between the landmarks and traces for efficiently constructing fine-grained floorplan. However, these methods are always restricted by the sparse landmark distribution or may cause privacy leakage. In this paper, we propose CrowdX, a crowdsourcing system for accurate, low-cost indoor floorplan construction enhanced with opportunistic encounters among mobile users. The key insight is that the spatial relation (i.e., the displacement of each user and the distance between each other during the encounter) will be extracted from the audio and inertia data, which are aligned by the proposed vibration event-based method. Such information can be used to calibrate the drift of encounter position. The calibrated encounter position is beneficial to most of the floorplan generation steps, such as trace drift elimination, landmark positioning, hallway assembling, and room area estimation. Our experiments in three shopping malls show that CrowdX achieves an average F-measure around 89.4%. In addition, the average estimated room area error within about 20%. The evaluation results demonstrate a significant improvement of accuracy enhanced with opportunistic encounters. © 2018 Association for Computing Machinery.

关键词： Crowdsourcing

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Capturing Global Structural Information in Long Document Question Answering with Compressive Graph Selector Network

Capturing Global Structural Information in Long Document Que...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Nie, Yuxiang Huang, Heyan Wei, Wei Mao, Xian-Ling School of Computer Science and Technology Beijing Institute of Technology China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China Beijing Institute of Technology Southeast Academy of Information Technology China Huazhong University of Science and Technology China

Long document question answering is a challenging task due to its demands for complex reasoning over long text. Previous works usually take long documents as non-structured flat texts or only consider the local structure in long documents. However, these methods usually ignore the global structure of the long document, which is essential for long-range understanding. To tackle this problem, we propose Compressive Graph Selector Network (CGSN) to capture the global structure in a compressive and iterative manner. The proposed model mainly focuses on the evidence selection phase of long document question answering. Specifically, it consists of three modules: local graph network, global graph network and evidence memory network. Firstly, the local graph network builds the graph structure of the chunked segment in token, sentence, paragraph and segment levels to capture the short-term dependency of the text. Secondly, the global graph network selectively receives the information of each level from the local graph, compresses them into the global graph nodes and applies graph attention to the global graph nodes to build the long-range reasoning over the entire text in an iterative way. Thirdly, the evidence memory network is designed to alleviate the redundancy problem in the evidence selection by saving the selected result in the previous steps. Extensive experiments show that the proposed model outperforms previous methods on two datasets. © 2022 Association for Computational Linguistics.

关键词： Iterative methods

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simulation standardization: Current state and cross-platform system for network simulators 13th

Simulation standardization: Current state and cross-platform...

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13th International Conference on Mobile Ad-hoc and Sensor Networks, MSN 2017

作者： Latif, Zohaib Sharif, Kashif Alvi, Maria K. Li, Fan School of Computer Science Beijing Institute of Technology Beijing China Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China School of Electrical Engineering and Computer Science National University of Sciences and Technology Islamabad Pakistan

ISBN: (纸本)9789811088896

The amount of research done in the field of mobile ad hoc networks is extraordinarily large. Evaluation of protocols designed for ad hoc networks is challenging as the cost of node deployment in terms of resources required is high, hence, most of the researchers use simulations for performance evaluation. In this paper we address the pitfalls of simulation studies in ad hoc routing protocols published in recent years. We have conducted a survey to evaluate the current state of simulation studies published in top conference/journals of the communication domain. In majority of the published papers (the way simulation results are reported) we have found design flaws, unrealistic assumptions, are non-reproducible, and statistically invalid results. We also propose a standardizing architecture for automating the reporting and replication process for network simulators. This platform independent architecture alleviates the challenge of simulation parameter reporting and facilitates in designing better network simulation experiments. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Mobile ad hoc networks

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