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10th IEEE Smart World Congress, SWC 2024

作者： Wu, Ling Li, Xinyu Yang, Yingjie Guo, Kun Zhang, Qishan Fuzhou University College of Computer and Data Science Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing China De Montfort University School of Computer Science and Informatics Leicester United Kingdom Shanghai International Studies University College of Economics and Humanities Shanghai China

ISBN: (纸本)9798331520861

Real-world networks can be divided into homophilic and heterophilic graphs. Significantly, many nodes tend to be heterophilic in a homophilic graph. Graph representation learning in heterophilic graphs has attracted considerable significance in recent years as it breaks the homophily assumption and achieves higher-quality graph embeddings. However, existing heterophilic graph representation learning methods often mix attribute and topological information, neglecting the mutual exclusion between attribute information and topological information. Furthermore, algorithms in heterogeneous graphs usually rely on specific matrix structures to capture high-order information, which may restrict the applicability of these methods. To tackle these issues, this paper proposes a GNN-based strategy for Heterophilic graph Representation Learning based on Multi-order information extraction and High and Low pass filters (HRLMHL). First, we designed two relatively independent modules to learn attribute information and topological information, respectively, to address the issue of mismatch. Second, we built a multi-order information extraction module to extract multi-order similarity features, thereby achieving the learning of high-order graph information. Experiments on real datasets show that the node classification accuracy is superior to that of the state-of-the-art methods on most datasets. © 2024 IEEE.

关键词： Contrastive Learning

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Relaxing Distillation Constraints for Improved New Class Learning in Continual Semantic Segmentation

Relaxing Distillation Constraints for Improved New Class Lea...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Zheng Ti Xuze Hao Renhai Chen College of Intelligence and Computing Tianjin University Tianjin China School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Continual Semantic Segmentation (CSS) aims to continuously learn new classes while mitigating catastrophic forgetting. Existing CSS methods primarily address this challenge through knowledge distillation. While they focus on maintaining stability for old classes, this emphasis often restricts plasticity in learning new ones. In this work, we propose two plug-and-play methods to better balance stability and plasticity. First, for feature map distillation, we replace pixel-level constraints with statistical information extraction within a window, improving the learning of new classes while preserving structural integrity to overcome forgetting. Second, we use relative ranks of predictions to replace exact probabilistic values to loosen the constraints in the logit distribution of old classes. Extensive experiments demonstrate that our methods outperform state-of-the-art methods when integrating into several baselines across various CSS scenarios.

关键词： Semantic segmentation Semantics signal processing Feature extraction Probabilistic logic Information retrieval Acoustics Data mining Speech processing

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A Communication Protocol Identification Method Based on Time-Frequency Domain Combination for Internet of Things

A Communication Protocol Identification Method Based on Time...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Zhiwei Zheng Hui Zhi Kui Tie Xianpeng Wang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University School of Electronic and Information Engineering Anhui University Hefei China Zhongdian Kesiyi Technology (Anhui) Co. Ltd Bengbu China

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

At present, Internet of Things (IoT) testing instruments and meters are unable to identify the communication protocol of communication data when all characteristic parameters of communication data are unknown. Regarding this problem, this paper utilizes the K-Nearest Neighbors (KNN) model in machine learning, designs communication protocol identification methods and proposes a communication protocol identification method based on time-frequency domain combination (CPI- TFC). First, we generate time-domain waveforms and frequency-domain waveforms from sampling communication data sequences, and attempt only time-domain identification method and only frequency-domain identification method based on KNN model. Then, we compare the results, design a communication protocol identification method based on the combination of time-domain identification and frequency domain identification, and propose CPI-TFC. Experimental results show that CPI-TFC can achieve higher identification accuracy than only time-domain identification method and only frequency-domain identification method, so it can effectively identify the communication protocol for a IoT communication data. Moreover, CPI- TFC can provide prior information for subsequent configuring parameters in IoT testing instruments, improve the parameter configuration efficiency.

关键词： Meters Time-frequency analysis Protocols Accuracy Simulation Machine learning Nearest neighbor methods Internet of Things Time-domain analysis Testing

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A Study on the Recognition of Single-Word Tones of Yunnan Zhaotong Dialect Based on Speech signal Analysis

A Study on the Recognition of Single-Word Tones of Yunnan Zh...

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Language Technology and Digital Humanities (LTDH), International Conference on

作者： Hanyun Qi Shiliang Lyu Key Laboratory of Linguistic and Cultural Computing Ministry of Education Northwest Minzu University Key Laboratory of Minzu Language and Cultures Intelligent Information Processing Gansu Province Northwest Minzu University

ISBN: (数字)9798350380187

ISBN: (纸本)9798350380194

Zhaotong City is located in the northeast of Yunnan Province and used to be called “Zhuti County”. Due to the influence of population migration, the Zhaotong dialect has the characteristics of the northern dialect as well as its own unique features. Early research on the tones of Zhaotong dialect relied on traditional language survey methods. In this paper, we use modern methods of speech signal analysis to reanalyze the tones of Zhaotong dialect by means of spectral analysis of sound waveforms, resonance peak analysis, fundamental frequency extraction, etc. combined with the knowledge of experimental phonetics. It is concluded that the Zhaotong dialect consists of four tone categories, namely, Tone1, Tone2, Tone3, and Tone4. Among the single character tones, Tonel is characterized by a flat tonal pattern; Tone2 is characterized by a descending tonal pattern; Tone3 is characterized by a descending tonal pattern; and Tone4 is characterized by a zigzag tonal pattern.

关键词： Surveys Hands Fluctuations Urban areas Speech recognition Turning Registers Digital preservation Speech processing Spectral analysis

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Acoustic Analysis of Long and Short Vowels of Eastern Yugur Language for Low Resource Speech Synthesis

Acoustic Analysis of Long and Short Vowels of Eastern Yugur ...

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Language Technology and Digital Humanities (LTDH), International Conference on

作者： Shiliang Lyu Hanyun Qi Yuwen Min Qianqian Ma Key Laboratory of Minzu Language and Cultures Intelligent Information Processing Northwest Minzu University Gansu Province Key Laboratory of Linguistic and Cultural Computing Ministry of Education Northwest Minzu University

ISBN: (数字)9798350380187

ISBN: (纸本)9798350380194

The articulation of long and short vowels is not only a change in duration, but also includes changes in the shape of the oral cavity to comprehensively affect the timbre. Since the low-resource phonetic data is very limited, especially the pronunciation of long and short vowels will be affected by the surrounding phonemes or syllables, which makes it difficult for the speech synthesis model to adequately learn and capture the differences of vowels, thus affecting the accuracy and naturalness of the synthesis of long and short vowels. The eastern Yugur language in China belongs to the Mongolian group of the Altaic language family, with an endangered language and limited phonetic resource data. If this endangered language is extinct, it can be restored through speech synthesis. In this article, we analyze the acoustics of long and short vowels in the Eastern Yugur language, and find out the differences in the distribution of acoustic parameters, as well as the differences in the parameters of males and females, so as to provide a reference for the synthesis of the Eastern Yugur language.

关键词： Time-frequency analysis Shape Resonant frequency Training data Phonetics Rhythm Resonance Acoustics Speech synthesis Timbre

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Prediction of piRNA-mRNA interactions based on an interactive inference network

Prediction of piRNA-mRNA interactions based on an interactiv...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Liu, Yajun Li, Ru Li, Aimin Fei, Rong Guo, Xie Wu, Fang-Xiang Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China University of Saskatchewan Division of Biomedical Engineering Department of Computer Science Department of Mechanical Engineering Saskatoon Canada

ISBN: (纸本)9798350337488

As the largest class of small non-coding RNAs, piRNAs primarily present in the reproductive cells of mammals, which influence post-transcriptional processes of mRNAs in multiple ways. Effective methods for predicting piRNA and mRNA target relationships can help identify piRNA functions, investigate the possibility of piRNAs as biomarkers and therapeutic targets. In this study, we propose a computational approach for classifying the relationships of piRNA-mRNA pairs based on an interactive inference network (IIN). First, we gather piRNA-mRNA target data, collect sequence data by position alignment, and construct a benchmark dataset. Furthermore, a reliable negative set is constructed by positive-unlabeled learning. Finally, we view a piRNA and a mRNA sequence as a premise and hypothesis sentence, respectively, and IIN model is used to predict the relationship between them. The experiments demonstrate that our method effectively characterizes piRNA-mRNA interaction and could be beneficial for researchers to investigate piRNA functions. © 2023 IEEE.

关键词： interactive inference network noncoding RNA piRNA-mRNA interactions positive-unlabeled learning

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Sparsity-aware Subband Adaptive Filtering in Impulsive Interferences

Sparsity-aware Subband Adaptive Filtering in Impulsive Inter...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Zhikai Gong Yingsong Li Baohe Pang Xueyan Li Wudang Xiao Liping Li Nikola Zlatanov College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Faculty of Computer and Engineering Sciences Innopolis University Russia

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

In this paper, we propose two new subband adaptive filtering (SAF) algorithms based on the cost function of the logistic distance metric cost function and use the proportionate proximal gradient algorithm to exploit the sparsity of the system fully. Compared with prior works, the proposed algorithms can handle many different situations better. Through computer simulations, we prove that the algorithm performs excellently in sparse systems disturbed by impulse noises. The algorithm can be used in areas like system identifications.

关键词： Measurement Adaptive systems Computer simulation Noise Filtering algorithms Cost function Information filters

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Open-vocabulary 3D Semantic Understanding via Affinity Neural Radiance Fields

Open-vocabulary 3D Semantic Understanding via Affinity Neura...

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Next Generation Data-driven Networks (NGDN), International Conference on

作者： Yujie Fan Huan Luo Computer and Data Science Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (数字)9798350388374

ISBN: (纸本)9798350388381

Three-dimensional semantic understanding using only several multi-view images can largely reduce the communication burden on the network. In addition, while point clouds are extensively studied for 3D scene understanding, utilization of multi-view image data offers rich visual details and texture information. However, challenges persist in lifting 2D semantic features to 3D space and leveraging language for segmentation. Inspired by recent advancements, this paper proposes a method that combines CLIP features and SAM masks to create a feature field capable of segmenting objects via natural language text across 2D multi-view and 3D Gaussian splatting. It offers a promising function for extracting 3D assets for game engines and the metaverse. Our method involves mask generation from video frames, extracting physical scales via RGB Nerf with masks, and organizing hierarchical information for semantic comprehension. In the training process, affinity features maintain scale properties and guide CLIP feature generation with auto weights blending for semantic robustness. A straightforward 3D splatting CLIP feature approach and canonical text methodology enhance query robustness across 2D multi-view and 3D splatting through relevance score calculation based on text CLIP features for inference. Experimental results demonstrate promising improvements in semantic understanding of 3D scenes.

关键词： Training Image segmentation Vocabulary Visualization Three-dimensional displays Semantics Natural languages Neural radiance field Feature extraction Robustness

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Y-Mol: A Multiscale Biomedical Knowledge-Guided Large Language Model for Drug Development

arXiv

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arXiv 2024年

作者： Ma, Tengfei Lin, Xuan Li, Tianle Li, Chaoyi Chen, Long Zhou, Peng Cai, Xibao Yang, Xinyu Zeng, Daojian Cao, Dongsheng Zeng, Xiangxiang College of Computer Science and Electronic Engineering Hunan University China Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Xiangtan University China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University China Xiangya School of Pharmaceutical Sciences Central South University China

Large Language Models (LLMs) have recently demonstrated remarkable performance in general tasks across various fields. However, their effectiveness within specific domains such as drug development remains challenges. To solve these challenges, we introduce Y-Mol, forming a well-established LLM paradigm for the flow of drug development. Y-Mol is a multiscale biomedical knowledge-guided LLM designed to accomplish tasks across lead compound discovery, pre-clinic, and clinic prediction. By integrating millions of multiscale biomedical knowledge and using LLaMA2 as the base LLM, Y-Mol augments the reasoning capability in the biomedical domain by learning from a corpus of publications, knowledge graphs, and expert-designed synthetic data. The capability is further enriched with three types of drug-oriented instructions: description-based prompts from processed publications, semantic-based prompts for extracting associations from knowledge graphs, and template-based prompts for understanding expert knowledge from biomedical tools. Besides, Y-Mol offers a set of LLM paradigms that can autonomously execute the downstream tasks across the entire process of drug development, including virtual screening, drug design, pharmacological properties prediction, and drug-related interaction prediction. Our extensive evaluations of various biomedical sources demonstrate that Y-Mol significantly outperforms general-purpose LLMs in discovering lead compounds, predicting molecular properties, and identifying drug interaction events. The source code is available at https://***/r/Y-Mol. © 2024, CC BY.

关键词： Semantics

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Using Rough Sets to Improve the High-dimensional Data Anomaly Detection Method Based on Extended Isolation Forest 26

Using Rough Sets to Improve the High-dimensional Data Anomal...

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

作者： Liu, Hanlin Zhou, Jiantao Li, Hua Inner Mongolia University College of Computer Science Hohhot China Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (纸本)9798350331684

Anomaly detection refers to the identification of data objects that deviate from the general data distribution. One of the important challenges in anomaly detection is handling high-dimensional data, especially when it contains a large number of redundant attributes. Although traditional Isolation Forest (iForest) and Extended Isolation Forest (EIF) have performed well in anomaly detection, the detection accuracy is still extremely influenced by redundant attributes. In this paper, we propose a two-tier architecture named RS-EIF, to alleviate the negative impact of redundant attributes. Specifically, the RS-EIF combines the rough set theory and EIF algorithm and provides an effective basis for the EIF-based anomaly detection method by removing redundant attributes with rough set theory. Experimental results show that the RS-EIF algorithm performs better than the iForest and EIF algorithms in terms of anomaly detection rate, and the results of the combination of rough set theory and EIF are more prominent compared to the other combination methods. © 2023 IEEE.

关键词： Anomaly detection

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