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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Jia, Zihe Xue, Peng Dai, Zhiqiang Gao, Qian Zhang, Xiaomeng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China

ISBN: (纸本)9798350330717

The development of the Internet has made people more closely related and has put forward higher requirements for recommendation models. Most recommendation models are studied only for the long-term interests of users. In this paper, the interaction time between the user and the item is introduced as auxiliary information in the model construction. Interaction time is used to determine users' long-term preferences and short-term preferences. In this paper, temporal features are extracted by building a convolutional gated recurrent unit with attention neural network (CNN-GRU-Attention). Firstly, for the problem of accurate feature extraction, CNN are constructed to extract higher-level and more abstract features of themselves and transform high-dimensional data into low-dimensional data;secondly, for the problem of social temporality, GRU are used to not only extract temporal information, but also effectively reduce gradient dispersion, making model convergence and training easier;finally, Graph Attention networks are used to aggregate the social relationship information of users and items respectively, which constitute the final feature representation of users and items respectively. In particular, a modified cosine similarity is used to reduce the error caused by data insensitivity when constructing the social information of the item. In this study, simulation experiments are conducted on two publicly available datasets (Epinions and Ciao), and the experimental results show that the proposed recommended model performs better than other social recommendation models, improving the evaluation metrics of MAE and RMSE by 1.06%-1.33% and 1.19%-1.37%, respectively. The effectiveness of the model innovation is proved. © 2023 IEEE.

关键词： convolutional gated recurrent unit graph attention networks neighborhood recommendation systems social networks

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Deep Learning Based Proactive Anomaly Detection for 5G Core Control Plane Network Function Interactions

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Tan, Yawen Liu, Jiajia Li, Yuanhao Wang, Jiadai Xidian University State Key Laboratory of Integrated Services Networks School of Cyber Engineering Xi'an710071 China Northwestern Polytechnical University National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Cybersecurity Shaanxi Xi'an710072 China

The Service-Based Architecture (SBA) introduced by 3GPP allows the control plane of 5G Core Network (CN) to function through a set of interconnected Network Functions (NFs), which offers significant benefits such as improved scalability, simplified operations, and efficient resource utilization. However, such a new transformation could also make critical NFs in 5G CN become more susceptible to attacks due to both internal vulnerabilities within the software-implemented NFs or the expanded interfaces brought by the SBA. Existing studies targeting 5G network security pay little attention to the detection of abnormal behaviors from control plane NFs. Therefore, in this paper, we propose 5GCGuard, an anomaly detection scheme aiming at identifying abnormal NF interactions in 5G CN. It employs a deep learning-based sequence model to learn normal interaction patterns and detects anomalies based on deviations from the learned pattern. Special designs including attention mechanism, multi-task learning, probabilistic labeling and automatic threshold decision are made to enhance its detection capabilities. We establish a cloud-native 5G testbed to evaluate the effectiveness of 5GCGuard. Extensive evaluation results show its superiority over comparison schemes. Case studies also demonstrate its ability to proactively detect various NF interaction anomalies, enabling swift action to block attacks and prevent further damage. © 2015 IEEE.

关键词： 5G mobile communication systems

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Spatial Commonsense Reasoning for Machine Reading Comprehension 9th

Spatial Commonsense Reasoning for Machine Reading Comprehen...

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19th International Conference on Advanced data Mining and Applications, ADMA 2023

作者： Lin, Miaopei Wang, Meng-xiang Yu, Jianxing Wang, Shiqi Lai, Hanjiang Liu, Wei Yin, Jian School of Computer Science and Engineering Sun Yat-sen University Guangzhou China School of Artificial Intelligence Sun Yat-sen University Zhuhai China China National Institute of Standardization Beijing100088 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

ISBN: (纸本)9783031466632

This paper studies the problem of spatial commonsense reasoning for the machine reading comprehension task. Spatial commonsense is the human-shared but latent knowledge of object shape, size, distance, and position. Reasoning this abstract knowledge can facilitate machines better perceive their surroundings, which is crucial for general intelligence. However, this valuable topic is challenging and has been less studied. To bridge this research gap, we focus on this topic and propose a new method to realize spatial reasoning. Given a text, we first build a potential reasoning graph based on its parsing tree. To better support spatial reasoning, we retrieve the related commonsense entities and relations from external knowledge sources, including the pre-trained language model (LM) and knowledge graph (KG). LM covers all kinds of factual knowledge and KG has abundant commonsense relations. We then propose a new fusion method called LEGRN (LM Edge-GNN Reasoner Networks) to fuse the text and graph. LEGRN adopts layer-based attention to integrate the LM text encoder and KG graph encoder, which can capture correlations between LM text context and KG graph structure. Considering that spatial relations involve a variety of attributes, we propose an attribute-aware inferential network to deduce the correct answers. To evaluate our approach, we construct a new large-scale dataset named CRCSpatial, consisting of 40k spatial reasoning questions. Experiment results illustrated the effectiveness of our approach. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Large dataset

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Unsupervised Granular-Ball Partitioning Algorithm Based on KL Divergence 11th

Unsupervised Granular-Ball Partitioning Algorithm Based on ...

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Xu, Xiaodong Cheng, Dongdong Huang, Jinlong Zhang, Sulan Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China College of Big Data and Intelligent Engineering Yangtze Normal University Chongqing408100 China

ISBN: (纸本)9783031927461

Granular-ball computing is an important model developed in the field of granular computing in recent years. This method uses granular-balls to represent and cover the sample space, and learns based on granular balls. In the current unsupervised granular-ball partitioning method, the DM (Distribution measure) value or a fixed value is mainly used to measure the quality of the granular-ball. The former can clearly represent the data distribution, but resulting in a larger number of granular-balls obtained. The latter may not clearly express the data distribution. We hope to obtain fewer granular-ball while maintaining clear expression of data distribution. To address this, we propose an unsupervised granular-ball partitioning algorithm based on KL divergence. This method evaluates granular-ball quality by calculating the KL divergence between the granular-ball and a Gaussian-distributed control ball, leading to more reasonable divisions. To evaluate the algorithm’s performance, we combine it with the minimum spanning tree-based clustering method and compare it with other unsupervised granular-ball partitioning methods. The experimental results demonstrate that this method effectively balances the efficiency and accuracy of granular-ball partitioning, resulting in more suitable granular-ball representations and improving the effectiveness of the partition. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Granular computing

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An MRC Framework for Semantic Role Labeling 29

An MRC Framework for Semantic Role Labeling

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29th International Conference on Computational Linguistics, COLING 2022

作者： Wang, Nan Li, Jiwei Meng, Yuxian Sun, Xiaofei Qiu, Han Wang, Ziyao Wang, Guoyin He, Jun Key Laboratory of Data Engineering and Knowledge Engineering of MOE School of Information Renmin University of China China Shannon.AI Zhejiang University China Tsinghua University China University of Leeds United Kingdom Amazon Alexa AI

Semantic Role Labeling (SRL) aims at recognizing the predicate-argument structure of a sentence and can be decomposed into two sub-tasks: predicate disambiguation and argument labeling. Prior work deals with these two tasks independently, which ignores the semantic connection between the two tasks. In this paper, we propose to use the machine reading comprehension (MRC) framework to bridge this gap. We formalize predicate disambiguation as multiple-choice machine reading comprehension, where the descriptions of candidate senses of a given predicate are used as options to select the correct sense. The chosen predicate sense is then used to determine the semantic roles for that predicate, and these semantic roles are used to construct the query for another MRC model for argument labeling. In this way, we are able to leverage both the predicate semantics and the semantic role semantics for argument labeling. We also propose to select a subset of all the possible semantic roles for computational efficiency. Experiments show that the proposed framework achieves state-of-the-art or comparable results to previous work. Code is available at https://***/ShannonAI/MRC-SRL. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Semantics

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Using Depth-Enhanced Spatial Transformation for Student Gaze Target Estimation in Dual-View Classroom Images

Using Depth-Enhanced Spatial Transformation for Student Gaze...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haonan Miao Peizheng Zhao Yuqi Sun Fang Nan Xiaolong Zhang Yaqiang Wu Feng Tian School of Computer Science and Technology Xi’an Jiaotong University Xi’an China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Dual-view gaze target estimation in classroom environments has not been thoroughly explored. Existing methods lack consideration of depth information, primarily focusing on 2D image information and neglecting the latent 3D spatial context, which could lead to suboptimal transformation and cause the gaze cone to intersect with an incorrect object. This paper introduces a novel dual-view gaze target estimation method tailored for classroom settings, leveraging depth-enhanced spatial transformations. By formulating a depth-enhanced 2D space, our method uses depth-enhanced spatial transformation to accurately project students’ gaze cones to the teacher-oriented image. Additionally, we collected a dataset named DVSGE, specifically for student gaze target estimation in dual-view classroom images. Experimental results demonstrate significant performance improvements of 9.8% in AUC and 19.9% in L2-Distance for our method, surpassing existing methods.

关键词： Three-dimensional displays Estimation Focusing Signal processing Acoustics Speech processing

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PENCIL: Prototype-ENhanced ComposItional Learning for Class-Incremental Hand Gesture Recognition

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IEEE Transactions on Consumer Electronics 2025年

作者： Li, Zheng Zhang, Xueyi Guo, Yanming Cai, Siqi Lao, Mingrui National University of Defense Technology Laboratory for Big Data and Decision Changsha China National University of Singapore College of Design and Engineering Singapore Singapore National University of Defense Technology National Key Laboratory of Information Systems Engineering Changsha China

Gesture recognition systems, as an indispenable component of virtual reality technology, are typically required to continuously acquire and update knowledge in order to meet users demands for recognizing both historical and newlyregistered gestures in real-world applications. In this paper, we propose a novel yet effective Prototype-ENhanced ComposItional Learning (PENCIL) method for Class-Incremental Hand Gesture Recognition (CI-HGR) task. It attempt to achieve exemplarfree class-incremental learning through adaptive isolated parameter composition and fine-tuning. Specifically, the Task-Specific Compositional Mechanism (TCM) module in PENCIL designs the trainable indexable embedding for each sequentially-arrived tasks containing new gestures, enabling HGR model to dynamically composite different parameters based on diverse gesture inputs. Furthermore, to address the recognition confusion caused by strong similarities among different gestures representations, PENCIL involves the second module Prototype-Enhanced Hybrid Regularization (PHR). It leverage prototype pseudo samples to maintain the accuracy and stability of parameter composition in TCM. Extensive experiments demonstrate the efficacy and modelagnostic capability of our method. Moreover, PENCIL fulfils state-of-the-art performance, with remarkable accuracy boosts ( 13.33% and 22.43%) and overhead reduction (over 90%) in contrast to most competitive method. © 1975-2011 IEEE.

关键词： Virtual environments

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Recurring Drift Detection and Model Selection-Based Ensemble Classification for data Streams with Unlabeled data

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New Generation Computing 2021年第2期39卷 341-376页

作者： Li, Peipei Wu, Man He, Junhong Hu, Xuegang Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei 230009 China School of Computer Science and Information Engineering Hefei University of Technology Hefei 230009 China CloudWalk Technology Co. Ltd. Guangzhou China

data stream classification is widely popular in the field of network monitoring, sensor network and electronic commerce, etc. However, in the real-world applications, recurring concept drifting and label missing in data streams seriously aggravate the difficulty on the classification solutions. And this challenge has received little attention from the research community. Motivated by this, we propose a new ensemble classification approach based on the recurring concept drifting detection and model selection for data streams with unlabeled data. First, we build an ensemble model based on the classifiers and clusters. To improve the classification accuracy, we use the ensemble model to predict each data chunk and partition clusters according to the distribution of predicted class labels. Second, we adopt a new concept drifting detection method based on the divergence of concept distributions between adjoining data chunks to distinguish recurring concept drifts. All historical new concepts will be maintained. Meanwhile, we introduce the time-stamp-based weights for base models in the ensemble model. In the selection of the base model, we consider the time-stamp-based weight and the divergence between concept distributions simultaneously. Finally, extensive experiments conducted on four benchmark data sets show that our approach can quickly adapt to data streams with recurring concept drifts, and improve the classification accuracy compared to several state-of-the-art classification algorithms for data streams with concept drifts and unlabeled data. © 2021, Ohmsha, Ltd. and Springer Japan KK, part of Springer Nature.

关键词： data stream classification Ensemble learning Recurring concept drift Unlabeled data

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Chinese Idiom Paraphrasing

arXiv

引用

arXiv 2022年

作者： Qiang, Jipeng Li, Yang Zhang, Chaowei Li, Yun Yuan, Yunhao Zhu, Yi Wu, Xindong School of Information Engineering Yangzhou University Jiangsu Yangzhou China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Anhui Hefei China

Idioms, are a kind of idiomatic expression in Chinese, most of which consist of four Chinese characters. Due to the properties of non-compositionality and metaphorical meaning, Chinese Idioms are hard to be understood by children and non-native speakers. This study proposes a novel task, denoted as Chinese Idiom Paraphrasing (CIP). CIP aims to rephrase idioms-included sentences to non-idiomatic ones under the premise of preserving the original sentence’s meaning. Since the sentences without idioms are easier handled by Chinese NLP systems, CIP can be used to pre-process Chinese datasets, thereby facilitating and improving the performance of Chinese NLP tasks, e.g., machine translation system, Chinese idiom cloze, and Chinese idiom embeddings. In this study, CIP task is treated as a special paraphrase generation task. To circumvent difficulties in acquiring annotations, we first establish a large-scale CIP dataset based on human and machine collaboration, which consists of 115,530 sentence pairs. We further deploy three baselines and two novel CIP approaches to deal with CIP problems. The results show that the proposed methods have better performances than the baselines based on the established CIP dataset. © 2022, CC BY.

关键词： Natural language processing systems

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Health risks of rare earth elements exposure:Impact on mitochondrial DNA copy number and micronucleus frequency

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Journal of Environmental Sciences 2025年第5期151卷 150-160页

作者： Shiyi Hong Yali Zhang Qiaojian Zhang Zekang Su Guiping Hu Li Wang Shanfa Yu Xiaojun Zhu Guang Jia Department of Occupational and Environmental Health Sciences School of Public HealthPeking UniversityBeijing 100083China School of Engineering Medicine and Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang UniversityBeijing 100083China Department of Toxicology School of Public HealthBaotou Medical CollegeBaotouInner Mongolia 014040China Henan Institute for Occupational Medicine Zhengzhou 450052China National Center for Occupational Safety and Health Beijing 102308China Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety School of Public HealthPeking UniversityBeijing 100083China

Toxic effects in terms of mitochondria and hereditary substances have been characterized in vitro for individual rare earth elements,while,the joint effects of mixed elements exposure in the population remain *** on the Occupational Chromate Exposure Dynamic Cohort of China,this study investigated the relationship between 15 blood rare earth elements(cerium,dysprosium,erbium,europium,gadolinium,holmium,lanthanum,lutetium,neodymium,praseodymium,samarium,terbium,thulium,yttrium,and ytterbium)and mitochondrial DNA copy number(MtDNACN)as well as peripheral blood lymphocyte micronucleus frequency(MNF).The elastic net was used to select elements highly correlated with effect indicators,whose dose-response relationships were further illustrated by restricted cubic *** kernel regressionwas employed to explore the combined effects of elements and the contributions of single *** results showed that most rare earth elementswere positively correlatedwith effect indicators,with yttrium showing the strongest association(β(95%CI):0.139(0.1089–0.189)for MtDNACN,0.937(0.345–1.684)for MNF).In the mixed exposure model,with the exposure level fixed at the 50th percentile as the reference,the effect estimates on MtDNACN and MNF increased by 0.228 and 0.598 units,respectively,at the 75th *** single effect analysis implied that yttrium,lanthanum and terbium contributed the most to the elevation of MtDNACN,while yttrium posed the highest risk for genetic damage,accordingly,we provided recommendations to prioritize these elements of *** addition,we observed a chief mediating effect of MtDNACN on the elevation of MNF caused by lanthanum,whereas further mechanistic exploration is required to confirm this finding.

关键词： Rare earth elements Mitochondrial DNA copy number Micronucleus frequency Mixed effect

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