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2023 ieee International Conference on Big data, Bigdata 2023

作者： Sakaji, Hiroki Kaneda, Noriyasu Hokkaido University Research Faculty of Information Science and Technology Japan Bank of Japan Japan

ISBN: (纸本)9798350324457

In this study, we propose a methodology to extract, index, and visualize 'climate change narratives' (stories about the connection between causal and consequential events related to climate change). We use two natural language processing methods, BERT (Bidirectional Encoder Representations from Transformers) and causal extraction, to textually analyze newspaper articles on climate change to extract 'climate change narratives.' The novelty of the methodology could extract and quantify the causal relationships assumed by the newspaper's writers. Looking at the extracted climate change narratives over time, we find that since 2018, an increasing number of narratives suggest the impact of the development of climate change policy discussion and the implementation of climate change-related policies on corporate behaviors, macroeconomics, and price dynamics. We also observed the recent emergence of narratives focusing on the linkages between climate change-related policies and monetary policy. Furthermore, there is a growing awareness of the negative impacts of natural disasters (e.g., abnormal weather and severe floods) related to climate change on economic activities, and this issue might be perceived as a new challenge for companies and governments. The methodology of this study is expected to be applied to a wide range of fields, as it can analyze causal relationships among various economic topics, including analysis of inflation expectation or monetary policy communication strategy. © 2023 ieee.

关键词： Causal Chain Climate Change Narrative Economics

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Accelerating Big data Application by Eliminating Redundancy on Hadoop Cluster 29

Accelerating Big Data Application by Eliminating Redundancy ...

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29th ieee International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Lei, Kelun Du, Shaokang You, Xin Xuan, Zhibo Kong, Haoran Yang, Hailong Shang, Jing Xiao, Zhiwen Wu, Zhihui Luan, Zhongzhi Qian, Depei Beihang University School of Computer Science and Engineering Beijing China China Mobile Information Technology Center China

ISBN: (纸本)9798350330717

Big data applications are widely adopted to mine valuable information from a tremendous amount of industry data, which is commonly represented as a series of map-reduce operations. Among various map-reduce frameworks, Hadoop is most commonly adopted for data processing at large scale. Although Hadoop eases the development of highly scalable distributed big data applications, inefficient implementation due to poor coding practice and deep software abstractions can cause severe performance issues such as unreasonable slowdown, high response latency, and waste of computing resources, which can lead to unsatisfactory serving delay or significant maintenance cost. In this paper, we first categorize three common types of redundant patterns in big data applications. Then we propose a tool-assisted optimization workflow to detect the redundant patterns automatically, which profiles the application by sampling hardware performance monitoring units. Moreover, we present a profiling visualization method that can help to pinpoint the redundant codes. Based on these approaches, we optimize several big data applications by eliminating redundancies, yielding up to 14.8% performance improvement. © 2023 ieee.

关键词： Hadoop Performance Optimization Redundant Patterns

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Advancing Visible-Infrared Person Re-Identification: Synergizing Visual-Textual Reasoning and Cross-Modal Feature Alignment

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ieee TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2025年 20卷 2184-2196页

作者： Qiu, Yuxuan Wang, Liyang Song, Wei Liu, Jiawei Shi, Zhiping Jiang, Na Capital Normal Univ Informat Engn Coll Beijing 100048 Peoples R China Univ Sci & Technol China Dept Automat Hefei 230026 Peoples R China

Visible-infrared person re-identification (VI-ReID) is a critical cross-modality fine-grained classification task with significant implications for public safety and security applications. Existing VI-ReID methods primarily focus on extracting modality-invariant features for person retrieval. However, due to the inherent lack of texture information in infrared images, these modality-invariant features tend to emphasize global contexts. Consequently, individuals with similar silhouettes are often misidentified, posing potential risks to security systems and forensic investigations. To address this problem, this paper innovatively introduces natural language descriptions to learn the global-local contexts for VI-ReID. Specifically, we design a framework that jointly optimizes visible-infrared alignment plus (VIAP) and visual-textual reasoning (VTR), and introduces local-global joint measure (LJM) to enhance the metric, while proposing a human-LLM collaborative approach to incorporate textual descriptions into existing cross-modal person re-identification datasets. VIAP achieves cross-modal alignment between RGB and IR. It can explicitly utilize designed frequency-aware modality alignment and relationship-reinforced fusion to explore the potential of local cues in global features and modality-invariant information. VTR proposes pooling selection and dual-level reasoning mechanisms to force the image encoder to pay attention to significant regions based on textual descriptions. LJM proposes introducing local feature distances into the measure stage metric to enhance the relevance of matching using fine-grained information. Extensive experimental results on the popular SYSU-MM01 and RegDB datasets show that the proposed method significantly outperforms state-of-the-art approaches. The dataset is publicly available at https://***/qyx596/vireid-caption.

关键词： Feature extraction Natural languages Cognition Measurement data mining Video recording Identification of persons visualization Semantics Pedestrians Person re-identification modality alignment relation reasoning

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SpriteHyper2vec: A SCSPRITE data Completion Method Based on Hypergraph Random Walk 9

SpriteHyper2vec: A SCSPRITE Data Completion Method Based on ...

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9th ieee International Conference on Cloud Computing and Intelligence Systems, CCIS 2023

作者： Liu, Yamin Ye, Yusen Gao, Lin Xidian University 2 South Taibai Road School of Computer Science and Technology Shaanxi Xi'an710071 China

ISBN: (纸本)9798350304428

scSPRITE is an emerging method to capture multi-way chromatin interaction and generate high-resolution, genome-wide three dimensional (3D) genome organization in single cells. However, the data obtained from scSPRITE often contain missing values and exhibit sparsity, making it difficult to directly observe local structures of the 3D genome at the level of single cells or even small cell populations. Additionally, comprehending the chromatin conformation composition entails a large number of cell populations by scSPRITE with huge cost. Therefore, it is valuable to impute these missing values and extract higher-order structures using computational methods. Here, we propose SpriteHyper2vec, a single-cell imputation algorithm based on hypergraph, specifically designed for scSPRITE datasets, which effectively capture local and global topological structures by generalizing Node2vec method. When compared with original method, SpriteHyper2vec significantly improves the similarity between small cell populations and the benchmark data. Furthermore, the completion capability of SpriteHyper2vec enables the identification of A/B compartment-like structures even in small cell populations. In summary, SpriteHyper2vec effectively enhances the visualization and comparison of 3D organization in small cell populations based on scSPRITE data. © 2023 ieee.

关键词： Three dimensional computer graphics

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An Extended System for External Sensors data Acquisition and Validation During Conducting Polysomnography 22

An Extended System for External Sensors Data Acquisition and...

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22nd ieee Statistical Signal Processing Workshop (SSP)

作者： Choksatchawathi, Tanut Kaewlee, Thitikorn Sawadwuthikul, Guntitat Chaitusaney, Busarakum Jaimchariyatam, Nattapong Wilaiprasitporn, Theerawit Sudhawiyangkul, Thapanun Vidyasirimedhi Inst Sci & Technol Sch Informat Sci & Technol Rayong Thailand Sense AI Co Ltd Bangkok Thailand Korea Adv Inst Sci & Technol Sch Comp Daejeon South Korea King Chulalongkorn Mem Hosp Thai Red Cross Soc Excellence Ctr Sleep Disorders King WI USA

ISBN: (纸本)9781665452458

This paper proposes an extended system for acquiring external sensor data during polysomnography (PSG) tests. The proposed method intends to provide means for integrating external sensor devices with existing PSG systems in the sleep clinic without modification to the original system. The acquired external sensor data is temporally synchronized with the PSG data through a trigger signal and can be used in subsequent applications. Using our proposed system, we acquire ten additional channels via Polar Verity Sense. We validate the acquired data qualitatively through signal visualization. Then, we compare the derived heart rate (HR) from PPG with those from an electrocardiogram (ECG) in the PSG device to validate our system quantitatively. The result reveals a fair error between PPG and ECG HR, demonstrating an acceptable performance. In addition to the PPG data acquisition, the proposed method can be employed on different external sensors to produce various databases for sleep studies.

关键词： photoplethysmography (PPG) polysomnography (PSG) system integration sleep monitoring wearable sensors

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FV2ES: A Fully End2End Multimodal System for Fast Yet Effective Video Emotion Recognition Inference

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ieee TRANSACTIONS ON BROADCASTING 2023年第1期69卷 10-20页

作者： Wei, Qinglan Huang, Xuling Zhang, Yuan Commun Univ China Beijing 100000 Peoples R China

In the latest social networks, more and more people prefer to express their emotions in videos through text, speech, and rich facial expressions. Multimodal video emotion analysis techniques can help understand users' inner world automatically based on human expressions and gestures in images, tones in voices, and recognized natural language. However, in the existing research, the acoustic modality has long been in a marginal position as compared to visual and textual modalities. That is, it tends to be more difficult to improve the contribution of the acoustic modality for the whole multimodal emotion recognition task. Besides, although better performance can be obtained by introducing common deep learning methods, the complex structures of these training models always result in low inference efficiency, especially when exposed to high-resolution and long-length videos. Moreover, the lack of a fully end-to-end multimodal video emotion recognition system hinders its application. In this paper, we designed a fully multimodal video-to-emotion system (named FV2ES) for fast yet effective recognition inference, whose benefits are threefold: (1) The adoption of the hierarchical attention method upon the sound spectra breaks through the limited contribution of the acoustic modality and outperforms the existing models' performance on both IEMOCAP and CMU-MOSEI datasets;(2) the introduction of the idea of multi-scale for visual extraction while single-branch for inference brings higher efficiency and maintains the prediction accuracy at the same time;(3) the further integration of data pre-processing into the aligned multimodal learning model allows the significant reduction of computational costs and storage space.

关键词： Emotion recognition Feature extraction Acoustics visualization Transformers Deep learning data mining Multimodal emotion inference fully end-to-end

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A Lightweight Attention-Based Convolutional Neural Networks for Fresh-Cut Flower Classification

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ieee ACCESS 2023年 11卷 17283-17293页

作者： Fei, Yeqi Li, Zhenye Zhu, Tingting Ni, Chao Nanjing Univ Sci Technol ZiJin Coll Sch Intelligent Mfg Nanjing 210046 Peoples R China Nanjing Forestry Univ Coll Mech & Elect Engn Nanjing 210037 Jiangsu Peoples R China

In the process of classifying fresh-cut flowers, the classification accuracy of the algorithm plays a vital role in the control of quality stability, uniformity, and price of fresh-cut flowers, while the classification speed of an algorithm determines the possibility of industrial application. Currently, research on fresh-cut flower classification focuses on the breakthrough of classification accuracy, ignoring the real-time processing speed of the terminal, which seriously affects the use of fresh-cut flower online classification technology. In this study, RGB images and depth information data of 434 rose flowers were collected using a binocular stereo depth camera. Combined with the actual production line classification environment, a set of data argumentation solutions was developed under the condition of limited samples. The architecture was established and optimized based on the ShuffleNet V2 network backbone unit, transfer learning was performed, and an appropriate attention mechanism was invoked to classify flowers of five specifications. The experimental results showed that the proposed network structure had a competitive advantage in terms of parameter quantity, classification speed, and accuracy compared with traditional networks without an attention mechanism and other lightweight networks. The classification accuracy on the 3-channel (RGB channel) flower dataset and the 4-channel (RGB and depth channel) flower datasets were 98.891% and 99.915%, respectively, and the overall prediction classification speed can reach 0.020 seconds per flower. Compared to the fresh-cut flower classification machines currently on the market, the speed of the proposed method has a great advantage. These advantages are of great significance for the design and development of fresh-cut flower classification and grading systems, and the proposed method is instructive for the identification and application of multichannel data in the future.

关键词： Feature extraction Production Classification algorithms Image segmentation Plants data visualization Computer architecture Fresh-cut flower grading lightweight network attention mechanism image augmentation model parameter optimization

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Yolact-based Approach for Real-Time Fetal Heart Segmentation

Yolact-based Approach for Real-Time Fetal Heart Segmentation

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2023 International Conference on data science and Its Applications, ICoDSA 2023

作者： Sapitri, Ade Iriani Nurmaini, Siti Rachmatullah, Muhammad Naufal Darmawahyuni, Annisa Firdaus, Firdaus Islami, Anggun Universitas Sriwijaya Intelligent System Research Group Faculty of Computer Science Palembang Indonesia

ISBN: (纸本)9798350305197

The detection and identification of anomalies in fetal hearts require extensive expertise, clinical experience, and significant time. In this context, deep learning (DL) technology based on instance segmentation has become increasingly popular and effective in accelerating the detection of fetal cardiac anomalies. This research aims to develop a DL model based on instance segmentation using the YOLACT approach to detect fetal cardiac anomalies. Specifically, it focuses on anomalies in the septal wall of the fetal heart using ultrasound (USG) images. Fetal cardiac USG images are used as the dataset to train the DL model. The test results demonstrate that the proposed DL model based on the YOLACT approach is capable of detecting defects in the septal wall of the fetal heart with a mean average precision (mAP) of 93.44% and an intersection over union (IoU) of 82.02%. In this regard, the YOLACT technique enables the identification of abnormalities at the pixel level, providing detailed visualization of the affected areas. This research holds the potential to be a breakthrough in the field of fetal cardiac anomaly detection, as it can reduce dependence on experts and expedite the diagnostic process. By utilizing deep learning technology and the YOLACT approach, it is expected that there will be improvements in accuracy and efficiency in identifying fetal cardiac anomalies, thereby enabling early and better medical interventions for patients. © 2023 ieee.

关键词： Anomaly detection

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CANet: A Spatial Structure Constraint and Local Semantic Awareness Based Network for Weakly Supervised Building Extraction

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ieee TRANSACTIONS ON GEOscience AND REMOTE SENSING 2025年 63卷

作者： Wang, Siyuan Shao, Zhenfeng Hou, Dongyang Wang, Yu Wang, Jinyang Cai, Bowen Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan 430079 Peoples R China Cent South Univ Sch Geosci & Infophys Changsha 410000 Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China

Benefitting from the easy availability of image-level labels, weakly supervised semantic segmentation (WSSS) methods based on class activation maps (CAMs) have made significant progress in building extraction from remote sensing imagery. However, image-level labels lack precise spatial locations and boundary ranges of buildings, posing challenges in achieving comprehensive and structurally clear building extraction. Furthermore, due to the complex background interference and the diversity of building in high-resolution remote sensing imagery (HRRS), small and sparse buildings suffer from insufficient attention in CAMs. To solve the above problems, this article proposes a spatial structure constraint and local semantic awareness-based WSSS method, CANet, for extracting buildings from HRRS. Specifically, we design a spatial structure constraint module to generate CAMs with finer spatial structural details of buildings, which minimizes feature differences from patches of different granularities and the whole image. Moreover, a local semantic awareness module is designed to address the issue of insufficient coverage of CAMs on sparse and tiny building. This module first strengthens the feature extraction network by embedding discriminative suppression units to force the network to focus on more nondiscriminative regions. Subsequently, visual word learning is introduced to identify additional object categories. Finally, four WSSS datasets are constructed based on public datasets with two simple and two complex scenarios. The results demonstrate that the proposed method outperforms 11 state-of-the-art methods, improving the intersection over union by at least 3.46% and 0.29% in both simple and complex scenarios, respectively.

关键词： Cams Buildings Semantics Remote sensing Feature extraction data mining Accuracy Semantic segmentation Conditional random fields visualization Building extraction remote sensing self-supervised learning (SSL) weakly supervised deep learning

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A Statistical Physics Perspective: Understanding the Causality Behind Convolutional Neural Network Adversarial Vulnerability

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ieee TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年第2期36卷 2118-2132页

作者： Wang, Ke Zhu, Mingjia Chen, Zicong Weng, Jian Li, Ming Yiu, Siu-Ming Ding, Weiping Gu, Tianlong Jinan Univ Minist Educ Engn Res Ctr Trustworthy AI Guangzhou 510632 Peoples R China Jinan Univ Coll Cyber Secur Guangzhou 510632 Peoples R China Jinan Univ Coll Informat & Sci Guangzhou 510632 Peoples R China Jinan Univ Natl Joint Engn Res Ctr Network Secur Detect & Pr Guangzhou 510632 Peoples R China Jinan Univ Guangdong Key Lab Data Secur & Privacy Preserving Guangzhou 510632 Peoples R China Jinan Univ Guangdong Hong Kong Joint Lab Data Secur & Privac Guangzhou 510632 Peoples R China Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Nantong Univ Sch Informat & Sci Nantong 226019 Peoples R China

The adversarial vulnerability of convolutional neural networks (CNNs) refers to the performance degradation of CNNs under adversarial attacks, leading to incorrect decisions. However, the causes of adversarial vulnerability in CNNs remain unknown. To address this issue, we propose a unique cross-scale analytical approach from a statistical physics perspective. It reveals that the huge amount of nonlinear effects inherent in CNNs is the fundamental cause for the formation and evolution of system vulnerability. Vulnerability is spontaneously formed on the macroscopic level after the symmetry of the system is broken through the nonlinear interaction between microscopic state order parameters. We develop a cascade failure algorithm, visualizing how micro perturbations on neurons' activation can cascade and influence macro decision paths. Our empirical results demonstrate the interplay between microlevel activation maps and macrolevel decision-making and provide a statistical physics perspective to understand the causality behind CNN vulnerability. Our work will help subsequent research to improve the adversarial robustness of CNNs.

关键词： visualization Decision making Physics Neurons Neural networks Convolutional neural networks Mathematical models Adversarial vulnerability cascading failure causality convolutional neural network (CNN) statistical physics

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