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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 label 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 labeled 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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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 available 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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M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection

arXiv

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arXiv 2021年

作者： Wang, Junke Wu, Zuxuan Ouyang, Wenhao Han, Xintong Chen, Jingjing Lim, Ser-Nam Jiang, Yu-Gang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China Huya Inc Meta AI

The widespread dissemination of Deepfakes demands effective approaches that can detect perceptually convincing forged images. In this paper, we aim to capture the subtle manipulation artifacts at different scales using transformer models. In particular, we introduce a Multi-modal Multi-scale TRansformer (M2TR), which operates on patches of different sizes to detect local inconsistencies in images at different spatial levels. M2TR further learns to detect forgery artifacts in the frequency domain to complement RGB information through a carefully designed cross modality fusion block. In addition, to stimulate Deepfake detection research, we introduce a high-quality Deepfake dataset, SR-DF, which consists of 4,000 DeepFake videos generated by state-of-the-art face swapping and facial reenactment methods. We conduct extensive experiments to verify the effectiveness of the proposed method, which outperforms state-of-the-art Deepfake detection methods by clear margins. Copyright © 2021, The Authors. All rights reserved.

关键词： Frequency domain analysis

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A cloud-oriented two-layer database model and itsalgebraic operations

A cloud-oriented two-layer database model and itsalgebraic o...

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International Conference on Logistics, Informatics and Service Sciences (LISS)

作者： Ying Li Baotian Dong Guangxi Colleges and Universities Key Laboratory of Scientific Computing & Intelligent Information Processing Guangxi Teachers Education University Nanning China School of Traffic and transportation Beijing Jiaotong University Beijing China

ISBN: (纸本)9781509011032

The existed cloud data models cannot well meet the management requirements of structured data including a great deal of relational data, therefore a two-layer cloud data model is proposed by referencing the relation data model, Hierarchical data model and key value data model. In the representation layer, the concepts of composite class and composite object are proposed to represent nested data structure and its instance. In the storage layer, two different triples are proposed to represent non-nested data structure and its instance. The conception of Rule is proposed to represent the relations of the data, and the conception of CAO (Component-Attribute-Object) is proposed to represent the data in non-nested data instance. The proposed model is fit for the management requirements of structured data in the cloud for using nested data to avoid join operation and using key-value model to store data. Algebraic operations including set operations and query operations are defined by referencing the algebraic operations of relation data model based on the proposed model. The conception of simple nested object is proposed to be the operation operand for set operation and selecting operation; therefore these operations have simple expression and ensure a correct result. The conception of attribute tree is proposed and is applied in the query operation to offer attributes selection. Theoretical analysis and example validation show that the proposed database model is effective and its algebraic operations are correct.

关键词： Data models Databases Cloud computing Data structures Atomic layer deposition Publishing Computational modeling

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Design of People Flow Monitoring System in Public Place based on MD-MCNN

Design of People Flow Monitoring System in Public Place base...

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2020 International Conference on 5G Mobile Communication and information Science, MCIS-5G 2020

作者： Tianmin, Xiong Xiaochun, Lei Xingwen, Zheng Junyan, Chen Yizhou, Feng School of Computer and Information Security Guilin University of Electronic Technology Guilin541004 China Guangxi Colleges and Universities Key Lab. of Intelligent Processing of Computer Images and Graphics Guilin541004 China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guilin541004 China

Due to the limitation of hardware resources, the traditional people flow monitoring system based on computer vision in public places can't meet different crowd-scale scenarios. Therefore, a people flow monitoring system based on MD-MCNN algorithm is designed, which is an application system combining the improved SSD object detection algorithm MNSSD and MCNN density map regression algorithm. In the initial stage, the system uses MNSSD for accurate detection and counting. If the people flow gradually reaches a certain threshold, the system automatically uses MCNN to estimate people flow until the people flow falls below the threshold. Through the experimental verification, the system can realize the people flow statistics of low-density and high-density people in different scenarios, and can be applied on the existing embedded platform. This scheme can be extended to smart cities, smart scenic spots, smart transportation and other fields. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Object detection

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Quantitative composite decision-theoretic rough set

Quantitative composite decision-theoretic rough set

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International Conference on intelligent System and Knowledge Engineering, ISKE

作者： Linna Wang Ling Liu Xin Yang Pan Zhuo School of Electronic and Information Engineering Sichuan Technology and Business University Chengdu China Key Lab of Cloud Computing and Intelligent Information Processing Sichuan Technology and Business University Chengdu China

In practical decision-making, we prefer to characterize the uncertain problems with the hybrid data, which consists of various types of data, e.g., categorical, numerical, set-valued and interval-valued. The extended rough sets can deal with single types of data based on specific binary relation, including the equivalence relation, neighborhood relation, partial order relation, tolerance relation, etc. However, the fusion of these relations is a significant challenge task in such composite information table. To tackle this issue, this paper proposes the intersection and union composite relation, and further introduces a quantitative composite decision-theoretic rough set model. Moreover, we present a novel matrix-based approach to compute the upper and lower approximations in proposed model. Finally, an numerical example is conducted to illustrate the efficiency of proposed method.

关键词： Rough sets Numerical models Mathematical model Computational modeling Data models Probabilistic logic Business

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Retrieval Augmented Recipe Generation

arXiv

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arXiv 2024年

作者： Liu, Guoshan Yin, Hailong Zhu, Bin Chen, Jingjing Ngo, Chong-Wah Jiang, Yu-Gang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China Singapore Management University Singapore

The growing interest in generating recipes from food images has drawn substantial research attention in recent years. Existing works for recipe generation primarily utilize a two-stage training method—first predicting ingredients from a food image and then generating instructions from both the image and ingredients. Large Multi-modal Models (LMMs), which have achieved notable success across a variety of vision and language tasks, shed light on generating both ingredients and instructions directly from images. Nevertheless, LMMs still face the common issue of hallucinations during recipe generation, leading to suboptimal performance. To tackle this issue, we propose a retrieval augmented large multimodal model for recipe generation. We first introduce Stochastic Diversified Retrieval Augmentation (SDRA) to retrieve recipes semantically related to the image from an existing datastore as a supplement, integrating them into the prompt to add diverse and rich context to the input image. Additionally, Self-Consistency Ensemble Voting mechanism is proposed to determine the most confident prediction recipes as the final output. It calculates the consistency among generated recipe candidates, which use different retrieval recipes as context for generation. Extensive experiments validate the effectiveness of our proposed method, which demonstrates state-of-the-art (SOTA) performance in recipe generation on the Recipe1M dataset. © 2024, CC BY-NC-ND.

关键词： Food ingredients

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How to use open-pFind in deep proteomics data analysis?—A protocol for rigorous identification and quantitation of peptides and proteins from mass spectrometry data

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Biophysics Reports 2021年第3期7卷 207-226页

作者： Guangcan Shao Yong Cao Zhenlin Chen Chao Liu Shangtong Li Hao Chi Meng-Qiu Dong School of Life Sciences Peking University National Institute of Biological Sciences Beijing Tsinghua Institute of Multidisciplinary Biomedical Research Tsinghua University Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) University of CASInstitute of Computing TechnologyCAS University of Chinese Academy of Sciences Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine and EngineeringBeihang University

High-throughput proteomics based on mass spectrometry（MS） analysis has permeated biomedical science and propelled numerous research projects. p Find 3 is a database search engine for high-speed and in-depth proteomics data analysis. p Find 3 features a swift open search workflow that is adept at uncovering less obvious information such as unexpected modifications or mutations that would have gone unnoticed using a conventional data analysis pipeline. In this protocol, we provide step-by-step instructions to help users mastering various types of data analysis using p Find 3 in conjunction with p Parse for data pre-processing and if needed, p Quant for quantitation. This streamlined p Parse-p Findp Quant workflow offers exceptional sensitivity, precision, and speed. It can be easily implemented in any laboratory in need of identifying peptides, proteins, or post-translational modifications, or of quantitation based on15N-labeling, SILAC-labeling, or TMT/i TRAQ labeling.

关键词： Mass spectrometry Protein identification Search engine Open-pFind Quantitation

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Better filtration and augmentation for hierarchical phrase-based translation rules

Better filtration and augmentation for hierarchical phrase-b...

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48th Annual Meeting of the Association for Computational Linguistics, ACL 2010

作者： Wang, Zhiyang Lü, Yajuan Liu, Qun Hwang, Young-Sook Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing 100190 China HILab Convergence Technology Center C and i Business SKTelecom 11 Euljiro2-ga Jung-gu Seoul 100-999 Korea Republic of

ISBN: (纸本)9781617388088

This paper presents a novel filtration criterion to restrict the rule extraction for the hierarchical phrase-based translation model, where a bilingual but relaxed wellformed dependency restriction is used to filter out bad rules. Furthermore, a new feature which describes the regularity that the source/target dependency edge triggers the target/source word is also proposed. Experimental results show that, the new criteria weeds out about 40% rules while with translation performance improvement, and the new feature brings another improvement to the baseline system, especially on larger corpus. © 2010 Association for Computational Linguistics.

关键词： Translation (languages)

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