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Neural recovery machine for Chinese dropped pronoun

Frontiers of Computer science 2019年第5期13卷 1023-1033页

作者： Weinan ZHANG Ting LIU Qingyu YIN Yu ZHANG Research Center for Social Computing and Information Retrieval School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China

Dropped pronouns (DPs) are ubiquitous in pro-drop languages like Chinese, Japanese etc. Previous work mainly focused on painstakingly exploring the empirical features for DPs recovery. In this paper, we propose a neural recovery machine (NRM) to model and recover DPs in Chinese to avoid the non-trivial feature engineering process. The experimental results show that the proposed NRM significantly outperforms the state-of-the-art approaches on two heterogeneous datasets. Further experimental results of Chinese zero pronoun (ZP) resolution show that the performance of ZP resolution can also be improved by recovering the ZPs to DPs.

关键词： neural network Chinese dropped pronoun re-covery Chinese zero pronoun resolution

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A Fast Classification Approach to Upper-Limb Posture Recognition 13

A Fast Classification Approach to Upper-Limb Posture Recogni...

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2020 IEEE Congress on Cybermatics: 13th IEEE International Conferences on Internet of Things, iThings 2020, 16th IEEE International Conference on Green computing and Communications, GreenCom 2020, 13th IEEE International Conference on Cyber, Physical and Social computing, CPSCom 2020 and 6th IEEE International Conference on Smart Data, SmartData 2020

作者： Wu, Xueyan Jiang, Yinghang Liu, Qi Wu, Hao Liu, Xiaodong Nanjing University of Information Science Technology Nanjing China School of Computer and Software Nanjing University of Information Science Technology Nanjing China School of Computing Edinburgh Napier University Edinburgh Edinburgh United Kingdom

ISBN: (纸本)9781728176475

At present, the study of upper limb posture recognition is still in the primary stage, due to the diversity of the objective environment and the complexity of the human body posture, the upper limb posture has no Public dataset. In this paper, an upper extremity data acquisition system is designed, with a three-channel Data acquisition mode, collect acceleration signal and gyroscope signal as sample data. The data sets were pre-processed with de-weighting, interpolation, and feature extraction. With the goal of recognizing human posture, experiments with KNN, logistic regression, and random gradient descent algorithms were conducted. In order to verify the superiority of each algorithm, the data window was adjusted to compare the recognition speed, computation time and accuracy of each classifier. For the problem of improving the accuracy of human posture recognition, a neural network model based on full connectivity is developed. In the process of constructing the network model, the effects of different hidden layers, activation functions, and optimizers on the recognition rate were experimentally for the comparative analysis, the softplus activation function with better recognition performance and the adagrad optimizer are selected. Finally, by comparing the comprehensive recognition accuracy and time efficiency with other classification models, the fully connected neural network is verified in the human posture Superiority in Identification. © 2020 IEEE.

关键词： Data acquisition

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ODMGIS: An Outlier Detection Method Based on Multi-Granularity information Sets

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IEEE Transactions on Fuzzy Systems 2025年

作者： Zhang, Pengfei He, Zhaoxuan Wang, Dexian Jiang, Tao Li, Baolin Liu, Jia Huang, Wei Li, Tianrui The School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Chengdu611137 China The Acupuncture and Tuina School Chengdu University of Traditional Chinese Medicine Chengdu611137 China The School of Logistics Chengdu University of Information Technology Chengdu60020 China The School of Computer and Software Engineering Xihua University Chengdu610039 China The College of Computer and Data Science Fuzhou University Fuzhou35010 China The School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China

In the realm of data mining, outlier detection has emerged as a pivotal research focus, aimed at uncovering anomalies within datasets to extract meaningful and valuable insights. The objective is to leverage data mining methodologies to pinpoint anomalies within datasets, thereby revealing crucial and enlightening information. Herein, we introduce a groundbreaking outlier detection methodology, ODMGIS, that seamlessly integrates multi-granularity representation and information set concepts to devise the multi-granularity information set (MGIS) model. This model adeptly characterizes the distribution patterns of data points. Firstly, we employ entropy function and their complementary function as measurement tools to accurately quantify the inherent uncertainty in data with different distributions, and considers the sum of the two as a comprehensive representation of the overall uncertainty of the information source. Subsequently, an outlier score model is constructed based on MGIS, which can deeply characterize the degree of outlierness of samples, thereby effectively identifying abnormal points in the dataset. During validation, ODMGIS was rigorously tested on practical datasets from medicine and bioinformatics, and its performance was benchmarked against both traditional and the state-of-the-art algorithms, showcasing substantial benefits. This research not only contributes a fresh perspective to outlier detection but also sparks innovative avenues for exploring and advancing the granular computing theory. © 1993-2012 I IEEE. All rights reserved.

关键词： Granular computing

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Entity-enhanced Adaptive Reconstruction Network for Weakly Supervised Referring Expression Grounding

arXiv

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arXiv 2022年

作者： Liu, Xuejing Li, Liang Wang, Shuhui Zha, Zheng-Jun Li, Zechao Tian, Qi Huang, Qingming Institute of Computing Technology CAS Beijing100190 China University of Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518066 China The School of Information Science and Technology University of Science and Technology of China Hefei230027 China The School of Computer Science Nanjing University of Science and Technology Nanjing210094 China Huawei Cloud & AI Shenzhen518129 China The School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing100190 China The Institute of Computing Technology CAS Beijing100190 China

Weakly supervised Referring Expression Grounding (REG) aims to ground a particular target in an image described by a language expression while lacking the correspondence between target and expression. Two main problems exist in weakly supervised REG. First, the lack of region-level annotations introduces ambiguities between proposals and queries. Second, most previous weakly supervised REG methods ignore the discriminative location and context of the referent, causing difficulties in distinguishing the target from other same-category objects. To address the above challenges, we design an entity-enhanced adaptive reconstruction network (EARN). Specifically, EARN includes three modules: entity enhancement, adaptive grounding, and collaborative reconstruction. In entity enhancement, we calculate semantic similarity as supervision to select the candidate proposals. Adaptive grounding calculates the ranking score of candidate proposals upon subject, location and context with hierarchical attention. Collaborative reconstruction measures the ranking result from three perspectives: adaptive reconstruction, language reconstruction and attribute classification. The adaptive mechanism helps to alleviate the variance of different referring expressions. Experiments on five datasets show EARN outperforms existing state-of-the-art methods. Qualitative results demonstrate that the proposed EARN can better handle the situation where multiple objects of a particular category are situated together. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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Semi-MoreGAN: A New Semi-supervised Generative Adversarial Network for Mixture of Rain Removal

arXiv

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arXiv 2022年

作者： Shen, Yiyang Wang, Yongzhen Wei, Mingqiang Chen, Honghua Xie, Haoran Cheng, Gary Wang, Fu Lee School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China School of Computing and Decision Sciences Lingnan University Hong Kong Hong Kong School of Mathematics and Information Technology Education University of Hong Kong Hong Kong Hong Kong School of Science and Technology Hong Kong Metropolitan University Hong Kong Hong Kong

Real-world rain is a mixture of rain streaks and rainy haze. However, current efforts formulate image rain streaks removal and rainy haze removal as separated models, worsening the loss of image details. This paper attempts to solve the mixture of rain removal problem in a single model by estimating the scene depths of images. To this end, we propose a novel SEMI-supervised Mixture Of rain REmoval Generative Adversarial Network (Semi-MoreGAN). Unlike most of existing methods, Semi-MoreGAN is a joint learning paradigm of mixture of rain removal and depth estimation;and it effectively integrates the image features with the depth information for better rain removal. Furthermore, it leverages unpaired real-world rainy and clean images to bridge the gap between synthetic and real-world rain. Extensive experiments show clear improvements of our approach over twenty representative state-of-the-arts on both synthetic and real-world rainy images. Source code is available at https://***/syy-whu/Semi-MoreGAN. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Efficient UAV Path Planning and Energy Optimization with Cooperative System Model for Vehicular Adhoc Networks

Efficient UAV Path Planning and Energy Optimization with Coo...

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Innovative computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： A. Veeramuthu Shalini Mahalingam Ilakkia. T R. Sampathkumar T. D. Subha Department of Information Technology School of Computing Sathyabama Institute of Science and Technology Chennai Department of Computer Science and Engineering St.Joseph’s College of Engineering College Chennai Department of Electrical and Electronics Engineering Sona College of Technology Salem Tamilnadu India Department of Mechatronics Engineering KPR Institute of Engineering and Technology Coimbatore Department of Electronics and Communication Engineering R.M.K. Engineering College Chennai Tamilnadu

Vehicular Ad Hoc Networks (VANETs) is a highly trending technology which occupies lots of applications of intelligent communications in day to day life. Typically, vehicles in a vehicular network move at rapid speeds with unpredictable mobility, leading to frequent changes in the network’s structure. Consequently, this might cause some problems while transmitting data between vehicles. In addition, external activities might lead to data loss. In order to overcome certain drawbacks in recent times ground level to air level data connectivity is concentrated which includes the concept of Unmanned Aerial Networks (UAV). The primary obstacles in the construction of UAV is that its network formation and path finding process. For the purpose of overcoming these kinds of drawbacks in this article effective UAV path planning and the energy optimization (EUPEO) is concentrated in the vehicular communication. Through this process the communication is carried out in an effective manner with intelligent path planning of UAV and as well it reduces the delay and energy consumption. The idea is implemented in ns3 and from the final outcome it is obviously proven that the EUPEO method achieved superior results than previous methods concerned with efficiency and as well the delivery rate of the information transmitted.

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What Affects the Adoption of Metaverse in Education? A SEM-based Approach

What Affects the Adoption of Metaverse in Education? A SEM-b...

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International Joint Conference on Computer science and Software Engineering (JCSSE)

作者： Rohani Rohan Pranab Roy Vajirasak Vanijja Suree Funilkul Subhodeep Mukherjee Debajyoti Pal School of Information Technology King Mongkut's University of Technology Thonburi Bangkok Thailand Department of Computer Science and Engineering JK Laxmipat University Jaipur India Department of Operations Management GITAM (Deemed to be University) Visakhapatnam India Innovative Cognitive Computing Research Center KMUTT Bangkok Thailand

Metaverse is an emerging 3D digital space that augments the real world and the virtual world. It has been envisioned to be the trendsetter for future education having tremendous potential. However, the success of any new technology depends on its mass adoption and widespread societal diffusion. Therefore, it becomes important to understand the factors that can lead to its success by ensuring widespread adoption. In this work, we investigate the different psychological gratifications of the educational usage of Metaverse under the lens of the Uses and Gratification (UGT) theory. Particularly, we propose and test a research model by using the Structural Equation Modelling (SEM) approach by considering four fundamental psychological gratifications of autonomy, achievement, affiliation, and dominance, together with two additional factors of hedonic motivation and student personality. Data is collected from two Asian universities, and results show the importance of autonomy, hedonic motivation, and personality in predicting Metaverse adoption.

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Timeseries Anomaly Detection Using SAX and Matrix Profiles Based Longest Common Subsequence 23rd

Timeseries Anomaly Detection Using SAX and Matrix Profiles B...

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23rd International Conference on Computational science, ICCS 2023

作者： Nguyen, Thi Phuong Quyen Tran, Trung Nghia Giang, Hoang Ton Nu Huong Nguyen, Thanh Tung Faculty of Project Management The University of Danang- University of Science and Technology 54 Nguyen Luong Bang Danang Viet Nam 268 Ly Thuong Kiet Street District 10 Ho Chi Minh City Viet Nam Department of Information Systems and Data Analytics School of Computing National University of Singapore Singapore Singapore 268 Ly Thuong Kiet Street District 10 Ho Chi Minh City Viet Nam

ISBN: (纸本)9783031360206

Similarity search is one of the most popular techniques for time series anomaly detection. This study proposes SAX-MP, a novel similarity search approach that combines Symbolic Aggregate ApproXimation (SAX) and matrix profile (MP). The proposed SAX-MP method consists of two phases. The SAX method is used in the first phase to extract all of the subsequences of a time series, convert them to symbolic strings, and store these strings in an array. In the second phase, the proposed method calculates the MP based on the symbolic strings that are represented for all subsequences extracted in the first phase. Since a subsequence is represented by a symbolic string, the MP is calculated using a distance-based longest common subsequence rather than the z-normalized Euclidean distance. Top-k discords are detected based on the similarity MP. The proposed SAX-MP is implemented on several time series datasets. Experimental results reveal that the SAX-MP method is particularly effective at detecting anomalies when compared to HOT SAX and MP-based methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Anomaly detection

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LLMDet: Learning Strong Open-Vocabulary Object Detectors under the Supervision of Large Language Models

arXiv

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arXiv 2025年

作者： Fu, Shenghao Yang, Qize Mo, Qijie Yan, Junkai Wei, Xihan Meng, Jingke Xie, Xiaohua Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Tongyi Lab Alibaba Group China Peng Cheng Laboratory China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Guangdong Province Key Laboratory of Information Security Technology China China

Recent open-vocabulary detectors achieve promising performance with abundant region-level annotated data. In this work, we show that an open-vocabulary detector co-training with a large language model by generating image-level detailed captions for each image can further improve performance. To achieve the goal, we first collect a dataset, GroundingCap-1M, wherein each image is accompanied by associated grounding labels and an image-level detailed caption. With this dataset, we finetune an open-vocabulary detector with training objectives including a standard grounding loss and a caption generation loss. We take advantage of a large language model to generate both region-level short captions for each region of interest and image-level long captions for the whole image. Under the supervision of the large language model, the resulting detector, LLMDet, outperforms the baseline by a clear margin, enjoying superior open-vocabulary ability. Further, we show that the improved LLMDet can in turn build a stronger large multi-modal model, achieving mutual benefits. The code, model, and dataset is available at https://***/iSEE-Laboratory/LLMDet. Copyright © 2025, The Authors. All rights reserved.

关键词： Object detection

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A Blockchain-Based Interoperable Architecture for IoT with Selective Disclosure of information

A Blockchain-Based Interoperable Architecture for IoT with S...

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Pacific Rim International Symposium on Dependable computing

作者： Rahma Mukta Shantanu Pal Shailesh Mishra Hye-Young Paik Salil S. Kanhere Michael Hitchens School of Computer Science and Engineering University of New South Wales Sydney Australia School of Information Technology Deakin University Melbourne Australia School of Computer and Communication Sciences École Polytechnique Fédérale de Lausanne (EPFL) Switzerland School of Computing Macquarie University Sydney Australia

With the improvement of Internet of Things (IoT) technologies, services, and applications, there is a proliferation of access to smart devices in everyday life. However, granting access and controlling access rights for each resource is challenging in highly dynamic and large-scale IoT deployments. In particular, multiple access information may need to be provided to an entity when granting access rights to several resources. The situation becomes more complex when an entity is required to share its identity attribute to receive the access information. These raise the question of what identity information an entity needs to provide to obtain the required access to a particular resource and, subsequently, what access information needs to be provided when accessing that resource. That said, there is a need for a flexible approach where an entity can share a distinct identity and access attributes for accessing a resource without revealing additional information. Such flexibility in sharing information is significant given the privacy risk of an entity’s identity. This paper presents an architecture that delivers access rights to an entity with selective disclosure of information. Our approach ensures the minimum exchange of information (identity and access attribute) to enhance an entity’s privacy when granting access rights to an entity. We use blockchain to provide data authenticity (i.e., tamper-proof), transparency and automatic execution of access rights based on shared attributes using smart contracts. We implement a proof of concept of the proposed system using Hyperledger fabric as a permissioned blockchain network. Our results demonstrate the feasibility of the proposed system showing efficiency in granting access rights.

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