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

作者： Pan, Yudai Liu, Jun Zhang, Lingling Zhao, Tianzhe Lin, Qika Hu, Xin Wang, Qianying School of Computer Science and Technology Xi'an Jiaotong University China National Engineering Lab for Big Data Analytics China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering China Lenovo Research Beijing China

Relation prediction in knowledge graphs (KGs) aims at predicting missing relations in incomplete triples, whereas the dominant embedding paradigm has a restriction on handling unseen entities during testing. In the real-world scenario, the inductive setting is more common because entities in the training process are finite. Previous methods capture an inductive ability by implicit logic in KGs. However, it would be challenging to preciously acquire entity-independent relational semantics of compositional logic rules and to deal with the deficient supervision of logic caused by the scarcity of relational semantics. To this end, we propose a novel graph convolutional network (GCN)-based model LogCo with logical reasoning by contrastive representations. LogCo firstly extracts enclosing subgraphs and relational paths between two entities to supply the entity-independence. Then a contrastive strategy for relational path instances and the subgraph is proposed for the issue of deficient supervision. The contrastive representations are learned for a joint training regime. Finally, prediction results and logic rules for reasoning are attained. Comprehensive experiments on twelve inductive datasets show that LogCo achieves outstanding performance comparing with SOTA inductive baselines. © 2022 Association for Computational Linguistics.

关键词： Forecasting

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Design of Wrist Rehabilitation Robot 12

Design of Wrist Rehabilitation Robot

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12th International Conference on Information technology in Medicine and Education, ITME 2022

作者： Zhenfeng, Wang Key Laboratory of Big Data and Artificial Intelligence in University of Fujian Province MinNan Science and Technology University Quanzhou China

ISBN: (纸本)9798350310153

We have developed a wrist rehabilitation manipulator to help patients with wrist injuries during exercise achieve sustained and standardized rehabilitation. Based on the theory of rehabilitation medicine, a 3RRP spherical parallel three-dimensional exoskeleton repair manipulator was developed. According to the principle of ergonomics, the shape safety of the robot was optimized. The virtual prototype system is used to simulate the rehabilitation manipulator and MATLAB is used to operate the recovery manipulator. The simulation results show that the designed manipulator has three degrees of freedom, which can meet our needs. The working area is part of the spherical area, and there is no cavity. The appearance, color and humanistic dimension can enable patients to obtain better treatment experience. Conclusion: It provides some basis for the industrial design of manipulator. © 2022 IEEE.

关键词： Manipulators

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MUN: Image Forgery Localization Based on M3 Encoder and UN Decoder 39

MUN: Image Forgery Localization Based on M3 Encoder and UN D...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liu, Yaqi Chen, Shuhuan Shi, Haichao Zhang, Xiao-Yu Xiao, Song Cai, Qiang Beijing Electronic Science and Technology Institute China Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University China

ISBN: (纸本)157735897X

Image forgeries can entirely change the semantic information of an image, and can be used for unscrupulous purposes. In this paper, we propose a novel image forgery localization network named as MUN, which consists of an M3 encoder and a UN decoder. Firstly, the M3 encoder is constructed based on a Multi-scale Max-pooling query module to extract Multi-clue forged features. Noiseprint++ is adopted to assist the RGB clue, and its deployment methodology is discussed. A Multi-scale Max-pooling Query (MMQ) module is proposed to integrate RGB and noise features. Secondly, a novel UN decoder is proposed to extract hierarchical features from both top-down and bottom-up directions, reconstructing both high-level and low-level features at the same time. Thirdly, we formulate an IoU-recalibrated Dynamic Cross-Entropy (IoUDCE) loss to dynamically adjust the weights on forged regions according to IoU which can adaptively balance the influence of authentic and forged regions. Last but not least, we propose a data augmentation method, i.e., Deviation Noise Augmentation (DNA), which acquires accessible prior knowledge of RGB distribution to improve the generalization ability. Extensive experiments on publicly available datasets show that MUN outperforms the state-of-the-art works. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Legal Judgement Prediction via Contrastive Learning Based-Retrieval and Semantic Embedding

Legal Judgement Prediction via Contrastive Learning Based-Re...

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2024 IEEE International Symposium on Product Compliance Engineering-Asia, ISPCE-AS 2024

作者： Wei, Chaoyu Zhang, Qixuan Xu, Juan Wang, Yanling Huang, Zhenhua Hao, Tianyong School of Computer Science South China Normal University GuangZhou China Big Data Rule of Law Research Institute Nanjing University of Information Science & Technology Nanjing China School of Law South China Normal University GuangZhou China

ISBN: (纸本)9798331523008

Legal judgment prediction is a significant task in Legal Artificial Intelligence, aiming to predict judgment results based on fact descriptions automatically. The judgment output may include law articles, charges, prison terms, or combinations. Relevant legal cases with similar backgrounds or events should likewise be considered essential references for judgment, as they play an indispensable role in evaluating the prediction task. However, when applied to Chinese legal data, current methodologies often neglect the specialized preprocessing required for unique semantic attributes of Chinese, which affects their prediction performance. This paper proposes a novel contrastive learning- based model for legal judgment prediction named CLR-LJP to improve prediction performance. According to the legal judgment process in real life, law article prediction is converted as a multi- label classification task that includes both relative law cases and law articles. The CLR-LJP firstly converts fact descriptions, law articles, and charges into vectors by embedding Chinese semantics. Then, contrastive learning is introduced in retrieval training to distinguish the similar content of different label definitions. Finally, the role embedding method is applied to learn the interactions among multiple law articles to predict prison terms. Experiment results indicate that our CLR-LJP model achieves the best performance compared to the state-of-the-art models, especially in the task of prison term prediction. © 2024 IEEE.

关键词： Contrastive Learning

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data Augmentation for Tensor Completion via Embedding Gradient Tracking 10

Data Augmentation for Tensor Completion via Embedding Gradie...

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Deng, Han Li, Yuhui Xie, Songyou Yang, Yang Liu, Yaqin Big Data Development and Research Center Guangzhou College of Technology and Business Guangzhou528138 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

ISBN: (纸本)9798350312461

How can we enhance the performance of neural tensor completion models for sparse data recovery? The task of tensor completion is crucial for network monitoring since it is the basis of network operation and management. Neural tensor completion models can exploit the multi-dimensional structure of tensors to recover missing entries in partially observed tensors. However, they can produce inaccurate estimations as real-world tensors are very sparse. These models tend to overfit a small amount of data, leading to limited accuracy on recovered data. In this paper, we propose LightTracker, a general data augmentation framework that enhances the prediction accuracy of neural tensor completion models. Specifically, LightTracker first trains a neural tensor completion model and tracks the gradient from the view of embeddings. After training, we use the tracked entity gradient to determine the importance of unknown tensor entries. Then, we aggregate the entries' importance to calculate the importance of each entity. Finally, LightTracker creates an augmented tensor by mixing observed data and predicted entries with the highest importance. Experiments conducted on two network traffic datasets show that LightTracker effectively enhanced the imputation accuracy of different tensor completion baselines, showing its generality and effectiveness. © 2023 IEEE.

关键词： Embeddings

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Efficient Deep learning-based Real-time Migration Strategies for Containers in SDN 6

Efficient Deep learning-based Real-time Migration Strategies...

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6th International Conference on Next Generation data-Driven Networks, NGDN 2024

作者： Huang, Shan Liu, Yanan Li, Yanuo He, Feili Duan, Xiaodong School of Computer Science and Engineering Dalian Minzu University Dalian China Key Laboratory of Big Data Application Technology of the State Ethnic Affairs Commission Dalian China Dalian Ethnic Culture Digital Technology Key Laboratory Dalian China

ISBN: (纸本)9798350388374

Container technology is widely used and improve the efficiency of container real-time migration has become an important research topic. Existing studies mainly focus on optimizing iterative dumping of containers without fully considering the impact of migration node selection on migration efficiency. Software-defined networking (SDN), achieves centralized control of the network by separating the control plane and the forwarding plane. Applying deep learning to SDN makes tasks such as traffic prediction and network optimization more intelligent. This paper proposes Prediction Model based on CLCB (CNN-LSTM-CBAM) uses real-time collected load data as input, extracts feature components of load data using one-dimensional convolutional neural networks, and then predicts using LSTM. To improve prediction accuracy, a lightweight CBAM attention module is introduced, and the prediction results are output through fully connected layers. The Network Traffic Prediction Model based on GCGRU-Conv (GCN-GRU-CNN) considers both spatial and temporal features of network traffic. GCN and GRU are used to extract spatial and temporal features of network traffic, respectively, and then these features are fused together using convolutional neural networks. To address the gradient problem caused by increasing network depth, a residual network structure is introduced. Experimental results show that the method proposed can effectively reduce the container migration time and has better prediction results. © 2024 IEEE.

关键词： Convolutional neural networks

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Design of the Smart Bedside Table Based on Single-Chip Microcomputer 12

Design of the Smart Bedside Table Based on Single-Chip Micro...

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12th International Conference on Information technology in Medicine and Education, ITME 2022

作者： Shijia, Yang Weiqiang, Yang Xiehua, Yu Key Laboratory of Big Data and Artificial Intelligence University of Fujian Province MinNan Science and Technology University Quanzhou China

ISBN: (纸本)9798350310153

With the popularization of smart home, nightstand plays an increasingly important role in people's daily life. By means of questionnaire survey and data collection, it is found that most household nightstands only play the role of storage, the bedroom space is always not enough for all kinds of furniture, the nightstand cannot be interconnected with other smart devices. The existing smart nightstands on the market have common problems: uneven functions and low practicability. In view of the above problems, combined with the smart home system, this paper studies and designs a smart nightstand with HT66F2390 microcontroller as the core. To save the bedroom space, other furniture functions are integrated, such as adaptive adjustment of night light and brightness, refrigeration and fresh-keeping, heating and insulation, drawer fingerprint unlock, LED light group changing in different occasions. LCD displays the user interface for easy operation. The smart nightstand is set up to test the environment and optimize the functions of each module. The results show that the designed intelligent bedside table can integrate the functions of other furniture. It effectively saves bedroom space and optimizes living experience. The expected design goal has been achieved. © 2022 IEEE.

关键词： User interfaces

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SFFSlib:A Python library for optimizing attribute layouts from micro to macro scales in network visualization

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Chinese Physics B 2025年第5期34卷 124-138页

作者： Ke-Chao Zhang Sheng-Yue Jiang Jing Xiao College of Big Data and Internet Shenzhen Technology UniversityShenzhen 518118China School of Cyber Science and Technology University of Science and Technology of ChinaHefei 230026China College of Information and Communication Engineering Dalian Minzu UniversityDalian 116600China

Complex network modeling characterizes system relationships and structures,while network visualization enables intuitive analysis and interpretation of these ***,existing network visualization tools exhibit significant limitations in representing attributes of complex networks at various scales,particularly failing to provide advanced visual representations of specific nodes and edges,community affiliation attribution,and global *** limitations substantially impede the intuitive analysis and interpretation of complex network patterns through visual *** address these limitations,we propose SFFSlib,a multi-scale network visualization framework incorporating novel methods to highlight attribute representation in diverse network scenarios and optimize structural feature ***,we have enhanced the visualization of pivotal details at different scales across diverse network *** visualization algorithms proposed within SFFSlib were applied to real-world datasets and benchmarked against conventional layout *** experimental results reveal that SFFSlib significantly enhances the clarity of visualizations across different scales,offering a practical solution for the advancement of network attribute representation and the overall enhancement of visualization quality.

关键词： complex network visualization layout algorithm signed network fuzzy community structure social bot network

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Speech Encryption in Linear Canonical Transform Domain Based on Chaotic Dynamic Modulation

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Journal of Beijing Institute of technology 2021年第3期30卷 295-304页

作者： Liyun Xu Tong Zhang Chao Wen Institute of Big Data Science and Industry School of Computer and Information TechnologyShanxi UniversityTaiyuan 030006China

In order to transmit the speech information safely in the channel,a new speech encryp-tion algorithm in linear canonical transform(LCT)domain based on dynamic modulation of chaot-ic system is *** algorithm first uses a chaotic system to obtain the number of sampling points of the grouped encrypted *** three chaotic systems are used to modulate the corres-ponding parameters of the LCT,and each group of transform parameters corresponds to a group of encrypted ***,each group of signals is transformed by LCT with different ***-nally,chaotic encryption is performed on the LCT domain spectrum of each group of signals,to realize the overall encryption of the speech *** experimental results show that the proposed algorithm is extremely sensitive to the keys and has a larger key *** with the original signal,the waveform and LCT domain spectrum of obtained encrypted signal are distributed more uniformly and have less correlation,which can realize the safe transmission of speech signals.

关键词： communication security linear canonical transform transform domain encryption chaotic system

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CHA-Sens: An End-to-End Comprehensive Residual Convolution Framework for CSI-based Human Activity Sensing 26

CHA-Sens: An End-to-End Comprehensive Residual Convolution F...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Zhou, Fujia Zhang, Wei Zhu, Guangxu Li, Hang Shi, Qingjiang Tongji University School of Software Engineering Shanghai China Science and Technology on Electronic Information Control Laboratory Chengdu China Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (纸本)9798350331684

Channel state information (CSI)-based human activity recognition (HAR) receives increasing research interests due to its broad applications such as human-computer interaction, health care, and security surveillance. Deep learning (DL) methods have been widely adopted on CSI-based HAR tasks to extract features automatically, overcoming the complexity and unstableness of manual feature extraction process. However, many DL approaches fail to customize the designed model structure with the input CSI tensor shape, applying DL models recklessly. In addition, some researchers utilize attention mechanism yet independently along temporal, spatial, or frequency dimension. To address these issues, we propose an end-to-end comprehensive residual convolution framework, namely CHA-Sens, for general CSI-based human activity sensing. CHA-Sens consists of several comprehensive residual convolution modules (CRCM) that feature adaptive kernel size and stride, regional parameter-free attention mechanism and shortcut of identity mapping. Extensive experiments are conducted on three public CSI datasets for recognizing both single human activity (SHA) and human-to-human interaction (HHI) to show the superiority of the proposed design over other state-of-the-art benchmarks. © 2023 IEEE.

关键词： Channel state information

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