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Efficient Semantic Relationship and Representation Reconstruction Based on knowledge Graph

Efficient Semantic Relationship and Representation Reconstru...

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Electrical, Computer and Energy Technologies (ICECET), international conference on

作者： Ailin Song Huifeng Yin Shuai Zhong Mingkun Xu Guangdong Institute of Intelligence Science and Technology Zhuhai China University of Electronic Science and Technology of China Chengdu China Center for Brain-Inspired Computing Research (CBICR) Tsinghua University Beijing China

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

knowledge graph is pivotal in natural language processing, having reached a mature stage of development. They are comprised of interconnected synsets, forming a structured graph network with significant potential for leveraging in graph neural networks (GNNs). However, the extensive number of nodes within knowledge graphs often entails substantial computational demands during application. This study employs the GNNs to facilitate data reconstruction and dimensionality reduction. Inno-vatively, we utilize the Graph AutoEncoder (GAE) to introduce a fusion decoding approach, thereby implementing a dual decoding mechanism aimed at reconstructing intricate relationships and semantic features. Additionally, we employ an efficient multi-graph training methodology to mitigate the impact of complex structures on the training process. Our research contributes a novel solution for compressing and reconstructing knowledge graph data, fostering deeper integration between natural language processing and graph networks, and yielding promising outcomes.

关键词： Training knowledge engineering Dimensionality reduction Computational modeling Semantics knowledge graphs natural language processing Graph neural networks Decoding Optimization

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Tracked-Vehicle Retrieval by natural language Descriptions with Multi-Contextual Adaptive knowledge

Tracked-Vehicle Retrieval by Natural Language Descriptions w...

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2023 IEEE/CVF conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Le, Huy Dinh-Anh Qui-Vinh Nguyen, Quang Luu, Duc Trung Thi-Thanh Chau, Truc Chung, Nhat Minh Viet-Uyen Ha, Synh International University School of Computer Science and Engineering Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350302493

This paper introduces our solution for Track 2 in AI City Challenge 2023. The task is tracked-vehicle retrieval by natural language descriptions with a real-world dataset of various scenarios and cameras. Our solution mainly focuses on four points: (1) To address the linguistic ambiguity in the language query, we leverage our proposed standardized version for text descriptions for the domain-adaptive training and post-processing stage. (2) Our baseline vehicle retrieval model utilizes CLIP to extract robust visual and textual feature representations to learn the unified cross-modal representations between textual and visual features. (3) Our proposed semi-supervised domain adaptive (SSDA) training method is leveraged to address the domain gap between the train and test set. (4) Finally, we propose a multi-contextual post-processing technique that prunes out the wrong results based on multi-contextual attributes information that effectively boosts the final retrieval results. Our proposed framework has yielded a competitive performance of 82.63% MRR accuracy on the test set, achieving 1st place in the competition. Codes will be available at https://***/zef1611/AIC23-NLRetrieval-HCMIU-CVIP © 2023 IEEE.

关键词： Tracked vehicles

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From Text to Performance Measurement: Automatically Computing Process Performance Using Textual Descriptions and Event Logs 21st

From Text to Performance Measurement: Automatically Computin...

引用

21st international conference on Business Process Management (BPM)

作者： Resinas, Manuel del-Rio-Ortega, Adela van der Aa, Han Univ Seville SCORE Lab I3US Seville Spain Univ Mannheim Mannheim Germany

ISBN: (纸本)9783031416194;9783031416200

Process performance measurement assesses how well a process is running, covering various dimensions such as time, cost, and quality. This task involves the definition of measurable Process Performance Indicators (PPIs), which in many cases are calculated based on data recorded in an event log. An inhibitor of effective performance analysis is that establishing PPI definitions measurable from event logs is highly complex, because it requires process analytical expertise, as well as in-depth knowledge about the structure and contents of the available event data. Given that managers typically do not have such knowledge, this means that those stakeholders that are generally most interested in measuring process performance cannot do so in a convenient manner. Recognizing this, we bridge this gap by proposing an approach for the measurement of process performance based on textual descriptions and event logs, which combines state-of-the-art natural language processing techniques with matching strategies that are tailored to the task at hand. Evaluation experiments using textual descriptions provided by both industry and academic users demonstrate the accuracy of our approach.

关键词： Process performance measurement process mining natural language processing matching

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Human-Machine Interaction Systems for Training Industrial Robots

Human-Machine Interaction Systems for Training Industrial Ro...

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2024 OPJU international Technology conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Kumar, S Vinod Mishra, Dinesh Reddy, Uma Nagpal, Amandeep Kumar, Ashwani Al-Asadi, Zahraa Saad Institute of Aeronautical Engineering Hyderabad Dundigal India Cse Department M.P. Jabalpur India New Horizon College of Engineering Department of Artificial Intelligence and Machine Learning Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology College of Health and Medical Technology Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

Workplace robot processing speed and efficiency depend on human-machine interaction. This study investigated RLIL, ViTREX, and NLCCF, three innovative HMI approaches. The purpose is to improve workplace robot setup and use. RLIL uses copying and reinforcement learning to improve robot behaviour in real time. Workshops and assembly lines benefit from this flexible and precise solution. Computer vision lets ViTREX robots adapt to new environments. This makes moving and transport simpler. In circumstances where people need to utilize conventional language to lead robots, NLCCF makes it easier for people and robots to communicate. We assessed these methods' F1 accuracy, precision, and high score. RLIL was accurate and precise, making it suitable for precise outcomes. ViTREX balanced precision with memory, making it ideal for users who had to choose. The NLCCF was versatile and outperformed in every way. These cutting-edge HMI technologies provide more alternatives, faster decision-making, and greater communication, making them valuable in current workplaces. Considering memory, precision, and balance, the procedure should be suited to the application. These technologies might improve robot-human collaboration in manufacturing, enabling more efficient, flexible, and natural automation. © 2024 IEEE.

关键词： Collaborative robots

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Fusion of Stance Labels and Event knowledge for Effective Stance Detection

Fusion of Stance Labels and Event Knowledge for Effective St...

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natural language processing (ICNLP), international conference on

作者： Zhaoning Yin Yuanshuo Zhang Aohua Li Bo Chen Xiaobing Zhao National Language Resource Monitoring and Research Center of Minority Languages Language Information School of Information Engineering Minzu University of China Beijing China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

Stance detection on social media has attracted significant attention recently. However, for a hot event, it is not easy to determine the stance of a comment on a given target, because there is complex comment- target-event-stance relationship behind it. To alleviate this problem, a new fusion method is proposed, which integrates explicit stance label and brief event background into BERT-based stance classifier. By fine-tuning the BERT model, it learns to reason about accurate stances based on the fused information. Experiments on a Weibo stance detection dataset demonstrate the effectiveness of the method.

关键词： Accuracy Social networking (online) Blogs natural language processing

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LAL-JER: Label-Aware Learning for Adaptive Joint Entity and Relation Extraction with LLM data augmentation

LAL-JER: Label-Aware Learning for Adaptive Joint Entity and ...

引用

2023 international conference on Communication Network and Machine Learning, CNML 2023

作者： He, Meng Bai, Yunli College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot010018 China

ISBN: (纸本)9798400716683

Joint entity and relation extraction has achieved great improvements in natural language processing (NLP) and has been widely applied, such as constructing knowledge graph, query understanding and question answering. Existing methods usually spend long time on fitting the models on certain datasets with given label type, which greatly lacks the ability of generalization. The model cannot make prediction on label types that have not seen in the training set. To address this issue, we propose to use prompt to incorporate the semantic meaning of the label type description. Furthermore, we use large language model to perform data augmentation to improve the robustness of our model during training. Extensive experiments and ablation study on two joint entity and relation extraction validates the effectiveness of our work on that: 1. Our methods achieved states of art performance on joint entity and relation extraction benchmark based on pretrained language model bert. 2. Our methods can help the model make predictions on label type unseen before given prompts. © 2023 ACM.

关键词： Semantics

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CoQ:AN Empirical Framework for Multi-hop Question Answering Empowered by Large language Models

CoQ:AN Empirical Framework for Multi-hop Question Answering ...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Qiang Huang Feng Huang DeHao Tao YueTong Zhao BingKun Wang YongFeng Huang College of Information Science and Engineering Xinjiang University Urumqi China School of Humanities Tsinghua University Beijing China Beijing University of Posts and Telecommunications Beijing China School of Information Engineering Zhengzhou College of Finance and Economics Zhengzhou China Department of Electronic Engineering Tsinghua University Beijing China Zhongguancun Laboratory Beijing China

Prompt-based Large language Models(LLMs) are surprisingly powerful in generating natural language reasoning steps or Chains of Thoughts(CoT) for multi-hop question answering(QA). However, LLMs struggle when they lack access to necessary knowledge or when the knowledge within their parameters is outdated. Additionally, LLMs that rely solely on CoT tend to generate hallucinations during the reasoning process. To address these dilemmas, we propose the Chain of Question (CoQ) framework, a novel multi-hop QA approach. This approach decomposes a complex original question into multiple sub-questions according to a CoT to retrieve knowledge from an external knowledge base. It then answers the question process based on the retrieved knowledge in accordance with a CoT. We design that each point of thought generated during the reasoning process be supported by the knowledge retrieved in the external knowledge base. Experiments show that CoQ is effective in reducing model hallucinations, leading to higher factual accuracy than CoT. On average, it reduces factual errors by 31% over CoT, and even by 38% on the two most commonly used models today.

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Image Captioning using Deep Learning Techniques 1

Image Captioning using Deep Learning Techniques

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1st IEEE international conference on Contemporary Computing and Communications, InC4 2023

作者： Gopu, Arunkumar Nishchal, Pratyush Mittal, Vishesh Srinidhi, Kuna VIT-AP University School of Computer Science and Engineering Andhra Pradesh Amaravati India

ISBN: (纸本)9798350335774

The automatic generation of image descriptions is leading the field of computer vision and natural language processing-based research. Image captioning is a key task that calls for a semantic understanding of the images and the capacity to create descriptions with right structure. Image captioning is a complex problem as it often demands accessing data that might not be visible in each scene. It will require logical thinking to evaluate or have in-depth knowledge about the object present in an image. In this study, we developed a multilayer Convolutional Neural Network to produce words that describe the images, and we used Long Short-Term Memory to accurately construct relevant sentences out of the words that are produced. To generate an accurate description, the Convolutional Neural Network (CNN) model first compares the targeted image against a huge dataset of training samples. In this study, we have used the Flickr 8k dataset. We have used the Bilingual Evaluation Understudy (BLEU) metric to determine how well our model is generating captions for the images. It evaluates the generated text that has been translated from one language to a different language to evaluate the effectiveness of the machine translation system. In this study, we have also used two pre-Trained models (VGG16, and XceptionV3) for comparative study. © 2023 IEEE.

关键词： Convolutional neural networks

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USER-CENTRIC EMOTION MODELING FOR NEXT-GENERATION RECOMMENDER SYSTEM

USER-CENTRIC EMOTION MODELING FOR NEXT-GENERATION RECOMMENDE...

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2023 IEEE international conference on Research Methodologies in knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Manju, Ms T. Nivethitha, R. Lavanya, P. Jayashakthi, R. Department of Information Technology Sri Sairam Engineering College Chennai India

ISBN: (纸本)9798350305708

The incoming environment, fueled by people around us, has caused a massive increase in information over the past few days. It is very beneficial for humans to have Personal Assistant Agents who can help them find relevant information. It is important that these agents consider not only their preferences but also their psychological states (like their emotions). By considering it, this paper describes an ongoing Recommender System application, which recommends the user's podcast or music based on the current emotional state of the user. The current emotional state of the user is identified in two ways facial emotion recognition and voice emotion recognition. Facial emotion recognition is done by examining the 68 landmark points of the face using fuzzy logic to classify the emotions. Voice recognition is done by converting the speech to the text format using natural language processing. This process includes Tokenization, Stemming, Lemmatization, Parts of the speech, and Named Entity Recognition. From this obtained tokens, identifying the sentimental words like win, lost, frustrated, and so on. Based on the emotion identified from this voice emotion recognition, the recommendation system recommends the songs or podcasts to the user. Thereby giving emotional support to the user when the user is feeling down by recommending motivational podcasts or songs. © 2023 IEEE.

关键词： 68 landmark haar cascade algorithm emotion recognition emotion-based recommendation system facial voice recognition fuzzy logic

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Zero-Shot Speech Emotion Recognition Using Generative Learning with Reconstructed Prototypes 48

Zero-Shot Speech Emotion Recognition Using Generative Learni...

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48th IEEE international conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Xu, Xinzhou Deng, Jun Zhang, Zixing Yang, Zhen Schuller, Bjorn W. Nanjing University of Posts and Telecommunications School of Internet of Things China University of Augsburg Embedded Intelligence for Health Care & Wellbeing Germany Agile Robots Ag Germany Hunan University College of Computer Science and Electronic Engineering China Nanjing University of Posts and Telecommunications School of Telecommunications and Information Engineering China Imperial College Glam - the Group on Language Audio & Music London United Kingdom

ISBN: (纸本)9781728163277

Zero-shot Speech Emotion Recognition (SER) enables machines to perceive unseen-emotional speech without knowing any samples from these emotional states, which is helpful in audio-based autonomous affective computing. However, existing works on zero-shot SER directly employ original prototypes and only consider inter-domain knowledge transfer through learning unseen-emotional classifiers. In this regard, we propose a zero-shot SER approach using generative learning with reconstructed prototypes in this paper. Within the proposed approach, we first reconstruct prototypes using the alignment from paralinguistic features to semantic prototypes. Then, generative learning is performed to build the connection from the reconstructed prototypes to the features. Afterwards, zero-shot experiments on emotional-speech data demonstrate that the proposed approach achieves better performance compared with the state-of-the-art approaches. © 2023 IEEE.

关键词： emotional prototypes generative learning Speech emotion recognition zero-shot learning

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