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arXiv 2020年

作者： Pourpanah, Farhad Wang, Ran Lim, Chee Peng Wang, Xi-Zhao Yazdani, Danial College of Mathematics and Statistics Guangdong Key Lab. of Intelligent Information Processing Shenzhen University China Department of Electrical and Computer Engineering University of Windsor Canada College of Mathematics and Statistics Shenzhen Key Lab. of Advanced Machine Learning and Applications Guangdong Key Lab. of Intelligent Information Processing Shenzhen University China Institute for Intelligent Systems Research and Innovation Deakin University Australia College of Computer Science and Software Engineering Guangdong Key Lab. of Intelligent Information Processing Shenzhen University China School of Computer Science and Engineering Southern University of Science and Technology China

The Artificial Fish Swarm Algorithm (AFSA) is inspired by the ecological behaviors of fish schooling in nature, viz., the preying, swarming and following behaviors. Owing to a number of salient properties, which include flexibility, fast convergence, and insensitivity to the initial parameter settings, the family of AFSA has emerged as an effective Swarm Intelligence (SI) methodology that has been widely applied to solve real-world optimization problems. Since its introduction in 2002, many improved and hybrid AFSA models have been developed to tackle continuous, binary, and combinatorial optimization problems. This paper aims to present a concise review of the continuous AFSA, encompassing the original ASFA, its improvements and hybrid models, as well as their associated applications. We focus on articles published in high-quality journals since 2013. Our review provides insights into AFSA parameters modifications, procedure and sub-functions. The main reasons for these enhancements and the comparison results with other hybrid methods are discussed. In addition, hybrid, multi-objective and dynamic AFSA models that have been proposed to solve continuous optimization problems are elucidated. We also analyse possible AFSA enhancements and highlight future research directions for advancing AFSA-based models. Copyright © 2020, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Feature Selection for Data Classification based on Binary Brain Storm Optimization

Feature Selection for Data Classification based on Binary Br...

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IEEE International Conference on Cloud computing and Intelligence Systems (CCIS)

作者： Farhad Pourpanah Ran Wang Xizhao Wang College of Mathematics and Statistics Shenzhen University China Shenzhen Key Lab. Of advanced machine learning and applications Shenzhen University China Guangdong Key Lab. of Intelligent Information Processing Shenzhen University China

ISBN: (数字)9781728138633

ISBN: (纸本)9781728138640

Brain Storm Optimization (BSO) is an effective population-based optimization method inspired by human brainstorming process. This paper proposes a new binary BSO algorithm (BBSO) to develop a new feature selection approach in order to reduce the number of selected features and/or improve the classification accuracy. Specifically, a new update rule mechanism for generating new solutions is proposed, which improves the convergence speed and reduces the possibility of immature convergence in the algorithm. Furthermore, a fuzzy ARTMAP (FAM) neural network, which is an incremental learning model, is utilized as a classification approach to evaluate the effectiveness of the selected feature subsets. The performance of the proposed method is compared with those from the original BSO, particle swarm optimization (PSO) and genetic algorithm (GA) on eight commonly used and well-known benchmark problems. The experimental results indicate the superiority of the BBSO as compared with other state-of-the-art feature selection methods.

关键词： Knowledge based systems

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ICT: A System Combination for Chinese Semantic Dependency Parsing 12

ICT: A System Combination for Chinese Semantic Dependency Pa...

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Joint Conference on Lexical and Computational Semantics

作者： Hao Xiong Qun Liu Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing 100190 China

ISBN: (纸本)9781622765027;1622765028

The goal of semantic dependency parsing is to build dependency structure and lab.l semantic relation between a head and its modifier. To attain this goal, we concentrate on obtaining better dependency structure to predict better semantic relations, and propose a method to combine the results of three state-of-the-art dependency parsers. Unfortunately, we made a mistake when we generate the final output that results in a lower score of 56.31% in term of lab.led Attachment Score (LAS), reported by organizers. After giving golden testing set, we fix the bug and rerun the evaluation script, this time we obtain the score of 62.8% which is consistent with the results on developing set. We will report detailed experimental results with correct program as a comparison standard for further research.

关键词： System Combination Chinese Semantic Dependency Parsing dependency DEPENDENCE Semantics Parsing bugs information technology information and communication technology International Criminal Tribunal

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Syntax-enhanced pre-trained model 59

Syntax-enhanced pre-trained model

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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： Xu, Zenan Guo, Daya Tang, Duyu Su, Qinliang Shou, Linjun Gong, Ming Zhong, Wanjun Quan, Xiaojun Jiang, Daxin Duan, Nan School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Microsoft Research Asia Beijing China Microsoft Search Technology Center Asia Beijing China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Key Lab. of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (纸本)9781954085527

We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa. Existing methods utilize syntax of text either in the pre-training stage or in the fine-tuning stage, so that they suffer from discrepancy between the two stages. Such a problem would lead to the necessity of having human-annotated syntactic information, which limits the application of existing methods to broader scenarios. To address this, we present a model that utilizes the syntax of text in both pre-training and fine-tuning stages. Our model is based on Transformer with a syntax-aware attention layer that considers the dependency tree of the text. We further introduce a new pre-training task of predicting the syntactic distance among tokens in the dependency tree. We evaluate the model on three downstream tasks, including relation classification, entity typing, and question answering. Results show that our model achieves state-of-the-art performance on six public benchmark datasets. We have two major findings. First, we demonstrate that infusing automatically produced syntax of text improves pre-trained models. Second, global syntactic distances among tokens bring larger performance gains compared to local head relations between contiguous tokens. © 2021 Association for Computational Linguistics

关键词： Syntactics

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ICT: System Description for CoNLL-2012 12

ICT: System Description for CoNLL-2012

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Joint Conference on Empirical Methods in Natural Language processing and Computational Natural Language Learning

作者： Hao Xiong Qun Liu Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing 100190 China

ISBN: (纸本)9781627484046

In this paper, we present our system description for the CoNLL-2012 coreference resolution task on English, Chinese and Arabic. We investigate a projection-based model in which we first translate Chinese and Arabic into English, run a publicly availab.e coreference system, and then use a new projection algorithm to map the coreferring entities back from English into mention candidates detected in the Chinese and Arabic source. We compare to a baseline that just runs the English coreference system on the supplied parses for Chinese and Arabic. Because our method does not beat the baseline system on the development set, we submit outputs generated by the baseline system as our final submission.

关键词： Arabic language English English language Projection algorithms Chinese Chinese philosophy Chinese art BASELINE information technology information and communication technology International Criminal Tribunal Coreference Resolution

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ICT: A Translation based Method for Cross-lingual Textual Entailment 12

ICT: A Translation based Method for Cross-lingual Textual En...

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Joint Conference on Lexical and Computational Semantics

作者： Fandong Meng Hao Xiong Qun Liu Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing 100190 China

ISBN: (纸本)9781622765027

In this paper, we present our system description in task of Cross-lingual Textual Entailment. The goal of this task is to detect entailment relations between two sentences written in different languages. To accomplish this goal, we first translate sentences written in foreign languages into English. Then, we use EDITS, an open source package, to recognize entailment relations. Since EDITS only draws monodirectional relations while the task requires bidirectional prediction, thus we exchange the hypothesis and test to detect entailment in another direction. Experimental results show that our method achieves promising results but not perfect results compared to other participants.

关键词： foreign language information technology information and communication technology International Criminal Tribunal open source Translations based Translation Translation Process

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The Interactive computing Model for Cyber Physical Society

The Interactive Computing Model for Cyber Physical Society

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Sixth International Conference on Semantics,Knowledge and Grids(第六届语义、知识与网格国际会议 SKG 2010)

作者： Xiaoping Sun Hai Zhuge Xiaorui Jiang Knowledge Grid Research Group Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences 100190 Beijing China

With the permeation of information technology intothe physical world and human society, we are on the way to a Cyber Physical Society (CPSocio). A CPSocio inter-connects the nature, the cyber space, and the society, so as to provide human beings with a better living and working environment. researches on CPSocio will lead to a new revolution of information science and technology. In realizing CPSocio, semantics is the most fundamental problem. It is the foundation of all the intelligent activities such as communication, interaction, and cooperation in CPSocio. We are facing various scientific challenges to understand and clarify principles and models of the computing in CPSocio. In this paper we focus on the interactive computing model of Cyber Physical Society, discussing a possible architecture and computing model that enable future intelligent distributed services in CPSocio. The proposed model focuses on the interactive computation emerged from self-interpretation, self-organization and interactions of individual objects. Selfinterpretation enables objects to actively express their own semantic information to others. Interaction among objects will help achieving computing solutions in open, dynamic, limited and heterogeneous environments with partial availab.e information and services. The model aims at providing a feasible framework for systematically tackling key problems and building intelligent services in CPSocio.

关键词： Semantics Computational modeling Data models Artificial intelligence Humans Object recognition Servers

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C*-tree: A Multi-Dimensional Index Structure for Resource Space Model

C*-tree: A Multi-Dimensional Index Structure for Resource Sp...

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Sixth International Conference on Semantics,Knowledge and Grids(第六届语义、知识与网格国际会议 SKG 2010)

作者： Qiang Zeng Hai Zhuge Knowledge Grid Research Group Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

Resource Space Model (RSM) is a semantic data model for specifying, organizing, and retrieving resources based on classification of *** efficient index mechanism is critical for its implementation. However, traditional index mechanisms are not well suitable to resource spaces because the spaces are discrete and hierarchical. This paper proposes a new multidimensional index structure C*-tree. It employs boxes to represent resources so that the containment of boxes can reflect the hierarchical structure of resources. The metric function defined in the C*-tree is able to gather close resources in the index structure. To avoid overlap between bounding boxes, a new restricted bounding box is further introduced. Moreover, the insertion algorithms based on the data partition approach can fully utilize characteristics of RSM. The C*-tree is scalab.e and efficient on both the number of dimension and the amount of data for different distributions.

关键词： Indexes Semantics Encoding Measurement Data models Approximation methods Partitioning algorithms

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Cyber Physical Society

Cyber Physical Society

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Sixth International Conference on Semantics,Knowledge and Grids(第六届语义、知识与网格国际会议 SKG 2010)

作者： Hai Zhuge Knowledge Grid Research Group Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences 100190 Beijing China

Natural physical space provides material basis for the birth and evolution of human beings and *** progress of human society has created the cyber space. With the rapid development of information technology, the cyber space is connecting physical space, social space and mental space to form a new world - Cyber Physical Society. The way to explore the cyber physical society is different from the way to explore the natural physical space and society. This paper describes the ideal of the Cyber Physical Society, and presents its distinguished characteristics and scientific issues. research on the Cyber Physical Society could lead to the revolution of society, science and technology.

关键词： Semantics Space technology Aerospace electronics Humans Materials Communities Sensors

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Protein Structure Prediction:Challenges,Advances,and the Shift of research Paradigms

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Genomics, Proteomics & Bioinformatics 2023年第5期21卷 913-925页

作者： Bin Huang Lupeng Kong Chao Wang Fusong Ju Qi Zhang Jianwei Zhu Tiansu Gong Haicang Zhang Chungong Yu Wei-Mou Zheng Dongbo Bu Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Changping Laboratory Beijing 102206China Microsoft Research AI4Science Beijing 100080China Huawei Noah’s Ark Lab Wuhan 430206China Zhongke Big Data Academy Zhengzhou 450046China Institute of Theoretical Physics Chinese Academy of SciencesBeijing 100190China

Protein structure prediction is an interdisciplinary research topic that has attracted researchers from multiple fields,including biochemistry,medicine,physics,mathematics,and computer *** researchers adopt various research paradigms to attack the same structure prediction problem:biochemists and physicists attempt to reveal the principles governing protein folding;mathematicians,especially statisticians,usually start from assuming a probability distribution of protein structures given a target sequence and then find the most likely structure,while computer scientists formulate protein structure prediction as an optimization problem-finding the structural conformation with the lowest energy or minimizing the difference between predicted structure and native *** research paradigms fall into the two statistical modeling cultures proposed by Leo Breiman,namely,data modeling and algorithmic ***,we have also witnessed the great success of deep learning in protein structure *** this review,we present a survey of the efforts for protein structure *** compare the research paradigms adopted by researchers from different fields,with an emphasis on the shift of research paradigms in the era of deep *** short,the algorithmic modeling techniques,especially deep neural networks,have considerably improved the accuracy of protein structure prediction;however,theories interpreting the neural networks and knowledge on protein folding are still highly desired.

关键词： Protein folding Protein structure prediction Deep learning Transformer Language model

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