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arXiv 2024年

作者： Huang, Muye Lai, Han Zhang, Xinyu Wu, Wenjun Ma, Jie Zhang, Lingling Liu, Jun School of Computer Science and Technology Xi’an Jiaotong University China MOE KLINNS Lab Xi’an Jiaotong University China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering China

Chart understanding enables automated data analysis for humans, which requires models to achieve highly accurate visual comprehension. While existing Visual Language Models (VLMs) have shown progress in chart understanding, the lack of high-quality training data and comprehensive evaluation benchmarks hinders VLM chart comprehension. In this paper, we introduce EvoChart, a novel self-training method for generating synthetic chart data to enhance VLMs’ capabilities in real-world chart comprehension. We also propose EvoChart-QA, a noval benchmark for measuring models’ chart comprehension abilities in real-world scenarios. Specifically, EvoChart is a unique self-training data synthesis approach that simultaneously produces high-quality training corpus and a high-performance chart understanding model. EvoChart-QA consists of 650 distinct real-world charts collected from 140 different websites and 1,250 expert-curated questions that focus on chart understanding. Experimental results on various open-source and proprietary VLMs tested on EvoChart-QA demonstrate that even the best proprietary model, GPT-4o, achieves only 49.8% accuracy. Moreover, the EvoChart method significantly boosts the performance of open-source VLMs on real-world chart understanding tasks, achieving 54.2% accuracy on EvoChart-QA. © 2024, CC BY.

关键词： Visual languages

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Analyzing the Financial Impact of ESG News Sentiment on ESG Finance Trends 10

Analyzing the Financial Impact of ESG News Sentiment on ESG ...

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10th International Conference on Platform technology and Service, PlatCon 2024

作者： Kim, Minjoong Kim, Sujin Kim, Yongsung Moon, Jihoon Dept. of ICT Convergence Soonchunhyang University Asan Korea Republic of Graduate School of Education Chungnam National University Daejeon Korea Republic of Dept. of Technology Education Chungnam National University Daejeon Korea Republic of Dept. of AI and Big Data Soonchunhyang University Asan Korea Republic of

ISBN: (纸本)9798350367874

This paper examines the interconnections between environmental, social, and governance (ESG) financial trends and the sentiment analysis of ESG-related news from 2019 to 2022. A substantial corpus of news articles and ESG management data is subjected to analysis employing a range of techniques, including term frequency-inverse document frequency (TF-IDF), latent Dirichlet allocation (LDA) topic modeling, and sentiment analysis tools such as valence-aware dictionary and sentiment reasoner (VADER) and bidirectional encoder representations from transformers (BERT). This analysis identifies significant trends and relationships between sentiment direction, ESG ratings, and financial performance. The results demonstrate that while ESG ratings do not have a robust direct correlation with financial metrics, sentiment analysis of ESG news offers insights into the potential impact of ESG management. This study underscores the necessity for standardized ESG metrics to enhance the clarity and efficacy of measuring the financial impact of ESG management. These insights are intended to inform the formulation of policies and strategies for future ESG management. © 2024 IEEE.

关键词： Decentralized finance

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Generating Targeted Universal Adversarial Perturbation against Automatic Speech Recognition via Phoneme Tailoring

Generating Targeted Universal Adversarial Perturbation again...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Yujun Chen, Yanqu Wang, Jiakai Hu, Jin Tao, Renshuai Liu, Xianglong State Key Laboratory of Complex & Critical Software Environment Beihang University China School of Computer Science and Engineering Beihang University China College of Computer Science Beijing University of Technology China Zhongguancun Laboratory China School of Computer and Information Technology Beijing Jiaotong University China Institute of Data Space Hefei Comprehensive National Science Center China

ISBN: (纸本)9798350368741

There is a growing concern about adversarial attacks against automatic speech recognition (ASR) systems. Although research into targeted universal adversarial examples (AEs) has progressed, current methods are constrained by inefficient exploitation of audio features, demonstrating insufficient attack ability and robustness in the physical world. To solve this problem, we propose a phoneme-tailored attack (PTA) to improve the quality of the generated AEs. Specifically, to improve attack ability, we propose a Diverse Audio Composition Enrichment method, which enhances the utilization of audio features through phoneme-level slicing and recombination. To adapt AEs to complex environments, we propose a Natural Noise Pattern Guidance method to align AEs with natural noise patterns to improve their robustness. Experiments show that our method achieves an average accuracy of more than 72.34% and 98% with and without a norm constraint, and also demonstrates excellent performance in terms of generalization across datasets and resilience to MP3 compression. © 2025 IEEE.

关键词： Automatic speech recognition natural noise pattern phoneme-level targeted universal adversarial examples

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Stacking-Based Attention Temporal Convolutional Network for Action Segmentation 48

Stacking-Based Attention Temporal Convolutional Network for ...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Yang, Liu Jiang, Yu Hong, Junkun Wu, Zhenjie Yang, Zhan Long, Jun Central South University School of Computer Science and Engineering China Central South University Big Data Institute Central South University China Central South University Network Resources Management and Trust Evaluation Key Laboratory of Hunan Province Changsha China

ISBN: (纸本)9781728163277

Action segmentation plays an important role in video understanding, which is implemented by frame-wise action classification. Recent works on action segmentation capture long-term dependencies by increasing temporal convolution layers in Temporal Convolution Networks (TCNs). However, high layers in TCNs are more coarse access to video features, resulting in the loss of fine-grained information for frame-wise action classification. To address the above issues, we propose a novel Attention-based Temporal Convolution (ATC) block to capture fine-grained information of temporal dependencies for frame-wise action classification by self-attention mechanism. Via stacking ATC blocks, we design a Stacking-based Attention Temporal Convolutional Network (SATC) to adaptively capture long-term and short-term dependencies, according to the semantic similarity of features on different temporal receptive fields simultaneously. The experimental results demonstrate that our SATC outperforms other baselines on all three challenging datasets: GTEA, 50Salads and Breakfast. © 2023 IEEE.

关键词： Convolution

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Learning dynamics of kernel-based deep neural networks in manifolds

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science China(Information sciences) 2021年第11期64卷 105-119页

作者： Wei WU Xiaoyuan JING Wencai DU Guoliang CHEN School of Computer Science Wuhan University Institute of Deep-sea Science and Engineering Chinese Academy of Sciences School of Computer Guangdong University of Petrochemical Technology Institute of Data Science City University of Macau College of Computer Science and Software Engineering Shenzhen University

Convolutional neural networks(CNNs) obtain promising results via layered kernel convolution and pooling operations, yet the learning dynamics of the kernel remain obscure. We propose a continuous form to describe kernel-based convolutions through integration in neural manifolds. The status of spatial expression is proposed to analyze the stability of kernel-based CNNs. We divide CNN dynamics into the three stages of unstable vibration, collaborative adjusting, and stabilized fluctuation. According to the system control matrix of the kernel, the kernel-based CNN training proceeds via the unstable and stable status and is verified by numerical experiments.

关键词： learning dynamics kernel-based convolution manifolds control model network stability

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Research on the Physical Mechanism of Glass-Forming Ability Based on Ensemble Deep Learning and Shap Method

SSRN

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SSRN 2023年

作者： Lin, Yuancheng Liang, Yongchao Institute of Advanced Optoelectronic Materials and Technology School of Big Data and Information Engineering Guizhou University Guiyang550025 China

Glass forming ability (GFA) of Metallic Glasses (MGs) is the result of the interaction of multiple factors. Most previous studies have only focused on characteristic temperatures, and established machine learning models have significant limitations due to their inability to fully understand the kinetics and topological structure of MGs. Therefore, three deep learning models are build using thermodynamics parameters such as glass transition temperature Tg, liquidus temperature Tl and onset crystallization temperature Tx, as well as kinetics and topological parameters such as total electronegative TEN, average atomic volume VA, atomic size difference d, and mixing entropy Sm. To predict GFA and explore the mechanism of physical impacts of different characteristics on GFA, artificial neural network (ANN), convolutional neural network (CNN), and long short-term memory network (LSTM) are utilized. Experimental results show that low Tl and high Tx effectively improve the GFA. Furthermore, high Sm promotes amorphous formation by ANN. © 2023, The Authors. All rights reserved.

关键词： Long short-term memory

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Reconfigurable architecture for heterogeneous multi-core and many-core architecture with IoT assistance

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International Journal of High Performance Systems Architecture 2021年第3-4期10卷 162-173页

作者： Xing, Xuefeng Cao, Jing Zhou, Hongtao Song, Lei Qiu, Yanan Northeast Petroleum University Heilongjiang Daqing163318 China The Key Laboratory for Oil Big Data and Intelligent Analysis of Heilongjiang Province Heilongjiang Daqing163318 China School of Mathematics and Information Science and Technology Hebei Normal University of Science and Technology Hebei Qinhuangdao066000 China

This paper discusses a multi-core real-time device reconfigurable and has a sequence of configurations, each lifted to a predetermined state, executing different functions performed by the process. Multiple cores enhance one's efficiency while malfunctioning and under the requirements of powerful applications and programs to run various operations simultaneously with more ease. In this paper, the reconfigurable architecture for heterogeneous multi-core architecture (RA-HMCA) framework is proposed. However, researchers intend to simplify the program by preventing redundancies from being introduced and decrease the risk of threads when satisfying all necessary real-time requirements. The normalisation process is usually used for the removal of redundancies. The suggested solution employs multi-integer linear programming (MILP) technology in the scan stage to have a viable task model. An optically reconfigurable POSIX-based program creates a procedural performance of this technology. A research paper implementation and successful assessment support and validates the findings predicted. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Internet of things

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Two-dimensional transition metal chalcogenide nanomaterials for cancer diagnosis and treatment

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Chinese Chemical Letters 2022年第10期33卷 4437-4448页

作者： Jingjing Wu Tingting Hu Guoping Zhao Anran Li Ruizheng Liang State Key Laboratory of Chemical Resource Engineering Beijing Advanced Innovation Center for Soft Matter Science and EngineeringBeijing University of Chemical TechnologyBeijing 100029China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Engineering MedicineBeihang UniversityBeijing 100191China Department of Orthopedics Guilin People's HospitalGuilin 541002China

For more than a decade,the exfoliation of graphene and other layered materials has led to a tremendous amount of research in two-dimensional(2D)materials,among which 2D transition metal chalcogenides(TMCs)nanomaterials have attracted much attention in a wide range of applications including photoelectric devices,lithium-ion batteries,catalysis,and energy conversion and storage owing to their unique photoelectric physical *** such large specific surface area,strong near-infrared(NIR)absorption and abundant chemical element composition,2D TMCs nanomaterials have become good candidates in biomedical imaging and cancer *** review systematically summarizes recent progress on 2D TMCs nanomaterials,which includes their synthesis methods and applications in cancer *** the end of this review,we also highlight the future prospects and challenges of 2D TMCs *** is expected that this work can provide the readers with a detailed overview of the synthesis of 2D TMCs and inspire more novel functional biomaterials based on 2D TMCs for cancer treatment in the future.

关键词： Two-dimensional(2D)nanomaterials Transition metal chalcogenides(TMCs) Synthesis method Biomedical imaging Cancer treatment

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Finding the Missing data: A BERT-inspired Approach Against Package Loss in Wireless Sensing

arXiv

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arXiv 2024年

作者： Zhao, Zijian Chen, Tingwei Meng, Fanyi Li, Hang Li, Xiaoyang Zhu, Guangxu Shenzhen Research Institute of Big Data China School of Computer Science and Engineering Sun Yat-sen University China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China

Despite the development of various deep learning methods for Wi-Fi sensing, package loss often results in noncontinuous estimation of the Channel State Information (CSI), which negatively impacts the performance of the learning models. To overcome this challenge, we propose a deep learning model based on Bidirectional Encoder Representations from Transformers (BERT) for CSI recovery, named CSI-BERT. CSI-BERT can be trained in an self-supervised manner on the target dataset without the need for additional data. Furthermore, unlike traditional interpolation methods that focus on one subcarrier at a time, CSI-BERT captures the sequential relationships across different subcarriers. Experimental results demonstrate that CSIBERT achieves lower error rates and faster speed compared to traditional interpolation methods, even when facing with high loss rates. Moreover, by harnessing the recovered CSI obtained from CSI-BERT, other deep learning models like Residual Network and Recurrent Neural Network can achieve an average increase in accuracy of approximately 15% in Wi-Fi sensing tasks. The collected dataset WiGesture and code for our model are publicly available at https://***/RS2002/CSI-BERT. Copyright © 2024, The Authors. All rights reserved.

关键词： Wi-Fi

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Unveiling the Cosmos: a Comprehensive Exploration of Key Factors Shaping Pulsar Candidates Using HTRU2 dataset Analysis and Predictions

Unveiling the Cosmos: a Comprehensive Exploration of Key Fac...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Jianqiong Huang Wenlong Guo Waqar Khan Yajun Xie Yuxin Wan School of Big Data Fuzhou University of International Studies and Trade Fuzhou China College of Electronics and Information Science Fujian Jiangxia University Fuzhou China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

Following the completion of the 500-metre aperture spherical radio telescope (FAST) and the launch of the 19-beam receiver survey project, the number of candidates obtained from pulsar searches has exhibited a notable increase, resulting in a significant expansion in the number of observable celestial objects and astronomical phenomena. It has furnished a substantial corpus of data for astronomical research. However, the vast quantity of pulsar candidate data presents a considerable challenge in identifying genuine candidates, due to the presence of numerous interfering signals. (1) The search for pulsar candidates is a complex and time-consuming process, necessitating the acquisition and analysis of a substantial amount of observational data; (2) The discovery of each new pulsar contributes to our understanding of extreme physical processes in the Universe. It is therefore necessary to search for new pulsar candidates on an ongoing basis. This paper examines the key factors influencing the identification of pulsar candidates, with a view to developing an efficient screening process. The High Time Resolution Universe Survey 2 (HTRU2) dataset from the University of California, Irvine (UCI or UC Irvine) platform was employed for data analysis and the construction of predictive analysis models. These were evaluated using classification metrics, including precision, recall, F1-score, and ROC-AUC curves. The latter were constructed using decision trees (DT), random forest (RF) and alpha-investing algorithms. The experimental results demonstrate that the three most critical factors affecting the pulsar candidates are the mean value of the integrated profile (Mean_IP), the excess kurtosis of the integrated profile (Excess_kurtosis_IP), and the skewness of the integrated profile (Skewness_IP).

关键词： Surveys Measurement Software algorithms Collaboration Prediction algorithms Kurtosis Classification algorithms Decision trees Random forests Physics

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