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International Conference on computer Supported Cooperative Work in Design

作者： Jinxiao Yang Xingang Wang Qingao Wang Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In recent years, cross-modal retrieval has a wide range of applications in fields such as information retrieval and multimedia processing, where the key is to find relevant data in another modality through one modal query. In cross-modal graphic retrieval, mutual attention mechanisms are commonly used to interact image and text features. However, this interaction mechanism makes it difficult to extract separate visual and textual features for subsequent indexing steps, especially in large-scale retrieval tasks. Furthermore, in image representation, non-semantic information in image features can lead to inaccuracies in the alignment of image and text features. Therefore this paper proposes a Transformer and Feature Enhancement Method (TSFE) for Image-Text Retrieval. The method better fuses the underlying features and the higher level features by using a hierarchical Transformer coding structure on two separate branches, image and text, to proofread and localise the final output features. The Feature Enhanced Attention (FEA) module is designed in order to infer non-semantic information in the image and enhance the features of salient regions. Channel-space attention and max pooling is introduced on top of Transformer encoder self-attention to filter redundant and interfering information from channel and spatial dimensions. Experiments on Flickr30k dataset and MS-COCO dataset in this paper show improved performance in cross-modal retrieval, thus validating the effectiveness and sophistication of the proposed method.

关键词： Visualization Semantics Pipelines Noise Image representation Transformers Feature extraction

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LESaET: Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER

LESaET: Low-Dimensional Embedding Method for Link Prediction...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Lichao Ding Jing Zhao Kai Lu Zenghao Hao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Knowledge graphs (KGs) provide a structured representation of the real world through entity-relation triples. However, current KGs are often incomplete, typically containing only a small fraction of all possible facts. This involves inferring missing content from existing information, a task known as link prediction. Existing methods in the field of link prediction struggle with controlling the dimensionality of embedding vectors or suffer from overly complex models. In order to tackle these challenges, we introduce a method in this paper, named Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER (LESaET). LESaET leverages both self-attention mechanisms and tensor factorization to learn expressive contextual-enhanced representations of KGs. Specifically, LESaET employs the multi-head self-attention mechanism of Transformer as an encoder to capture the mutual information between entities and relationships, and utilizes Enhanced-TuckER as a decoder, ultimately achieving expressive low-dimensional embeddings for link prediction tasks. LESaET demonstrates competitive performance compared to advanced methods on standard datasets.

关键词： Tensors Stacking Knowledge graphs Predictive models Transformers Vectors Decoding Standards Mutual information Periodic structures

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HGTA: News Recommendation Based on Hierarchical Granular Semantic Embeddings and Threshold Attention

HGTA: News Recommendation Based on Hierarchical Granular Sem...

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International Conference on computer Supported Cooperative Work in Design

作者： Yiwen Li Zhenyu Yang Zhijun Li Xiaoyang Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Personalized news recommendation helps users find interesting content among much news information. Most existing news recommendation techniques densely interact a user’s historical clicked news with candidate news to capture their reading interest. However, during this process, even if certain historical clicked news is weakly correlated with the candidate news, the model still interacts with it, generating more noise information when constructing user interests. Simultaneously, the news representation learning process using neural networks is a fine-grained to coarse-grained learning process; i.e., as the network deepens, the fine-grained original news information is easily lost. Therefore, to solve the above problems, this paper proposes a news recommendation model based on hierarchical granular semantic embeddings and threshold attention (HGTA). In HGTA, we propose a threshold attention module to filter unimportant historical clicked news by setting an attention score threshold, i.e., interacting only with historical clicked news that is strongly correlated with the candidate news when capturing user interests. Thus, the model attention is focused, and the noisy interaction information is reduced. Furthermore, when training the neural network, to prevent the loss of fine-grained information, we design a hierarchical granular information extraction module to capture both coarse- and fine-grained news features to better enrich the news semantics. Experiments conducted on the large real-world Microsoft News dataset (MIND) show that our model achieves highly competitive results in terms of popular metrics. Our code is published at the following URL: https://***/liyiweneven/HGTA.

关键词： Uniform resource locators Training Representation learning Federated learning Semantics Neural networks Noise

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Overview of EvaHan2024: The First International Evaluation on Ancient Chinese Sentence Segmentation and Punctuation 3

Overview of EvaHan2024: The First International Evaluation o...

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3rd Workshop on Language Technologies for Historical and Ancient Languages, LT4HALA 2024

作者： Li, Bin Chang, Bolin Xu, Zhixing Feng, Minxuan Xu, Chao Qu, Weiguang Shen, Si Wang, Dongbo School of Chinese Language and Literature Nanjing Normal University China Center for Language Big Data and Computational Humanities Nanjing Normal University China Faculty of Arts and Humanities University of Macau China School of Computer and Electronic Information Nanjing Normal University China School of Economics and Management Nanjing University of Science and Technology China College of Information Management Nanjing Agricultural University China

ISBN: (纸本)9782493814463

Ancient Chinese texts have no sentence boundaries and punctuation. Adding modern Chinese punctuation to theses texts requires expertise, time and efforts. Automatic sentence segmentation and punctuation is considered as a basic task for Ancient Chinese processing, but there is no shared task to evaluate the performances of different systems. This paper presents the results of the first ancient Chinese sentence segmentation and punctuation bakeoff, which is held at the Third Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) 2024. The contest uses metrics for detailed evaluations of 4 genres of unpublished texts with 11 punctuation types. Six teams submitted 32 running results. In the closed modality, the participants are only allowed to use the training data, the highest obtained F1 scores are respectively 88.47% and 75.29% in sentence segmentation and sentence punctuation. The perfermances on the unseen data is 10 percent lower than the published common data, which means there is still space for further improvement. The large language models outperform the traditional models, but LLM changes the original characters around 1-2%, due to over-generation. Thus, post-processing is needed to keep the text consistancy. © 2024 ELRA Language Resources Association: CC BY-NC 4.0.

关键词： Computational linguistics

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The computational content of super strongly nonexpansive mappings and uniformly monotone operators

arXiv

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arXiv 2023年

作者： Sipoş, Andrei Department of Computer Science Faculty of Mathematics and Computer Science University of Bucharest Academiei 14 Bucharest010014 Romania Simion Stoilow Institute of Mathematics The Romanian Academy Calea Grivit ¸ei 21 Bucharest010702 Romania Institute for Logic and Data Science Popa Tatu 18 Bucharest010805 Romania

Recently, Liu, Moursi and Vanderwerff have introduced the class of super strongly nonexpansive mappings as a counterpart to operators which are maximally monotone and uniformly monotone. We give a quantitative study of these notions in the style of proof mining, providing a modulus of super strong nonexpansiveness, giving concrete examples of it and connecting it to moduli associated to uniform monotonicity. For the supercoercive case, we analyze the situation further, yielding a quantitative inconsistent feasibility result for this class (obtaining effective uniform rates of asymptotic regularity), a result which is also qualitatively *** Codes 47H05, 47H09, 47J25, 03F10 Copyright © 2023, The Authors. All rights reserved.

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Opinion-Tree-guided Contrastive Learning for Aspect Sentiment Quadruple Prediction

Opinion-Tree-guided Contrastive Learning for Aspect Sentimen...

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International Conference on computer Supported Cooperative Work in Design

作者： Zhijun Li Zhenyu Yang Yiwen Li Xiaoyang Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In recent years, aspect sentiment quadruple prediction (ASQP) has become a popular task in the field of aspect-based sentiment analysis. When modeling comment statements, it is often necessary to consider structural information between sentiment labels. Existing approaches categorize these labels by encoding text and dependency tree structures separately and then incorporating their representations, but this approach fails to effectively capture the structural information between sentiment labels. To address this problem, we propose a method for ASQP called Opinion-Tree-guided Contrastive Learning (OTCL), which embeds structural information directly into the text instead of modeling it separately. Specifically, OTCL utilizes the opinion tree structure to guide the construction of positive samples of the input text during training. By associating the input text with its positive samples, the text encoder can learn to generate textual representations associated with the opinion tree structure. We validate the effectiveness of our method on two commonly used datasets.

关键词： Training Sentiment analysis Federated learning Contrastive learning Predictive models Encoding Task analysis

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Visual Programming for Artificial Intelligent and Robotic Application (VPAR) Framework

Frontiers in Artificial Intelligence and Applications

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Frontiers in Artificial Intelligence and Applications 2021年 343卷 64-80页

作者： Pongthanisorn, Goragod Viriyavit, Waranrach Charoenporn, Thatsanee Sornlertlamvanich, Virach Department of Computer Engineering Faculty of Engineering Thai-Nichi Institute of Technology Thailand School of ICT Sirindhorn International Institute of Technology Thammasat University Thailand Graduate School of Science and Engineering Chiba University Japan Faculty of Data Science Musashino University Japan Faculty of Engineering Thammasat University 99 Moo 18 Paholyothin Road Klong Nueng Klong Luang Pathumthani12120 Thailand

ISBN: (纸本)9781643682426

computer programming is popularized in 21st century education in terms of allowing intensive logical thinking for students. Artificial Intelligent and robotic field is considered to be the most attractive for programming today. However, for the first-time learners and novice programmers, they may encounter a difficulty in understanding the text-based style programming language with its special syntax, sematic, libraries, and the structure of the program itself. In this work, we proposed a visual programming environment for artificial intelligent and robotic application using Google Blockly. The development framework is a web application which is capable of using Google Blockly to create a program and translate the result of visual programming style to conventional text-based programming. This allows almost instant programming capability for learners of programming in such a complex system. © 2021 The authors and IOS Press.

关键词： Application programs

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Overcoming data Limitations and Cross-Modal Interaction Challenges in Medical Visual Question Answering

Overcoming Data Limitations and Cross-Modal Interaction Chal...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yurun Bi Xingang Wang Qingao Wang Jinxiao Yang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Medical Visual Question Answering (Med-VQA) is a sophisticated multimodal technology within the medical domain that utilizes natural language processing and artificial intelligence techniques. It serves as a tool in the medical field to provide information and answers, aiming to correctly respond to medical questions related to specific professional medical images. The primary goal is to assist patients in understanding their physical condition and provide decision support for healthcare professionals. While Generalized Visual Question Answering has seen significant advancements, challenges persist in the medical domain. Firstly, medical data has inherent limitations, such as the scarcity of annotated medical datasets and the complexity of medical images (including factors like noise, artifacts, and low contrast). Secondly, in terms of cross-modal fusion, there exists an intricate relationship between medical images and textual information. The complexity of the relationship between medical images and medical text, as well as the incorporation of global contextual details, pose challenges. To address these issues, this paper proposes the Feature-Enhanced Network with Conditional Mixing. This innovative approach aims to overcome the scarcity and complexity of medical images without the need for additional datasets. Simultaneously, to tackle the challenge of cross-modal fusion and information interaction and to address the complex relationship between medical images and text, we propose the Cross-Adaptive Fusion Network. Experimental results conducted on the VQA-RAD dataset demonstrate noticeable improvements in accuracy compared to mainstream Med-VQA models.

关键词： Visualization Accuracy Noise Neural networks Medical services Question answering (information retrieval) data models

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LightPRA: A Lightweight Temporal Convolutional Network for Automatic Physical Rehabilitation Exercise Assessment

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computers in Biology and Medicine 2024年 173卷 108382-108382页

作者： Sardari, Sara Sharifzadeh, Sara Daneshkhah, Alireza Loke, Seng W. Palade, Vasile Duncan, Michael J. Nakisa, Bahareh Research Centre for Computational Science and Mathematical Modelling Coventry University Coventry United Kingdom School of Information Technology Faculty of Science Engineering and Built Environment Deakin University Geelong Vic Australia Department of Computer Science Swansea University Swansea United Kingdom School of Mathematics and Data Science Emirates Aviation University Dubai United Arab Emirates Centre for Sport Exercise and Life Sciences Coventry University Coventry United Kingdom

Research evidence shows that physical rehabilitation exercises prescribed by medical experts can assist in restoring physical function, improving life quality, and promoting independence for physically disabled individuals. In response to the absence of immediate expert feedback on performed actions, developing a Human Action Evaluation (HAE) system emerges as a valuable automated solution, addressing the need for accurate assessment of exercises and guidance during physical rehabilitation. Previous HAE systems developed for the rehabilitation exercises have focused on developing models that utilize skeleton data as input to compute a quality score for each action performed by the patient. However, existing studies have focused on improving scoring performance while often overlooking computational efficiency. In this research, we propose LightPRA (Light Physical Rehabilitation Assessment) system, an innovative architectural solution based on a Temporal Convolutional Network (TCN), which harnesses the capabilities of dilated causal Convolutional Neural Networks (CNNs). This approach efficiently captures complex temporal features and characteristics of the skeleton data with lower computational complexity, making it suitable for real-time feedback provided on resource-constrained devices such as Internet of Things (IoT) devices and Edge computing frameworks. Through empirical analysis performed on the University of Idaho-Physical Rehabilitation Movement data (UI-PRMD) and KInematic assessment of MOvement for remote monitoring of physical REhabilitation (KIMORE) datasets, our proposed LightPRA model demonstrates superior performance over several state-of-the-art approaches such as Spatial–Temporal Graph Convolutional Network (STGCN) and Long Short-Term Memory (LSTM)-based models in scoring human activity performance, while exhibiting lower computational cost and complexity. © 2024 The Authors

关键词： Convolution

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Normal forms and representable functions in Moisil logic

arXiv

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arXiv 2023年

作者： Sipoş, Andrei Department of Computer Science Faculty of Mathematics and Computer Science University of Bucharest Academiei 14 Bucharest010014 Romania Simion Stoilow Institute of Mathematics The Romanian Academy Calea Grivitęi 21 Bucharest010702 Romania Institute for Logic and Data Science Popa Tatu 18 Bucharest010805 Romania

In this note, we determine, by a disjunctive normal form theorem, which functions on the standard n-nuanced Lukasiewicz-Moisilalgebra are representable by formulas and we show how this result may help in establishing ... 详细信息

关键词： Many valued logics

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