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SSRN 2022年

作者： Yuan, Pingpeng Han, Sheng Zang, Shaoqi Shi, Xuanhua Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

In the field of finance, data has the characteristics of temporal multi-frequency and heterogeneous high dimension, and the traditional knowledge graph is not suitable to express the temporal relation of financial data, so the financial temporal knowledge graph has become a research hotspot recently. There are a large number of temporal edges in the financial temporal knowledge graph. The use of general graph database management system to store financial knowledge graph data has problems such as large storage scale and slow update speed, and it cannot realize the traceability of financial data, so it cannot meet the needs of the financial field. In order to solve these problems, HyperBit, a storage system for efficient storage and management of financial data, is designed and implemented. Firstly, a temporal quad storage structure is proposed. Secondly, the Log Structured Cumulative Update (LSCU) is proposed to update financial temporal data efficiently. The update log module is designed in the system. When there is data to update, it is not directly updated in the data storage of the system, but recorded in the update log, and the system updates the content of the update log cumulatively. The system manages update logs by partition and chunk, which makes the log storage compact and independent. Besides, it is convenient for parallel processing and it improves the data update speed. Thirdly, a Bidirectional Backtracking by Final and Optimal Root (BBFOR) method is proposed to meet the requirements of efficient traceability of financial data. Unlike traditional version tracing method, BBFOR adopt both the snapshot and incremental methods which balance the space and time cost of version switching, and BBROF consider the version of the access frequency characteristic and set up the final root and the optimal root. Besides, BBROF adopt bidirectional backtracking technology to improve the efficiency of the version switching. Experimental results show that the storage cost

关键词： Switching

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Interactive Prototype Learning and Self-Learning for Few-Shot Medical Image Segmentation

SSRN

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SSRN 2024年

作者： Song, Yuhui Xu, Chenchu Wang, Boyan Du, Xiuquan Chen, Jie Zhang, Yanping Li, Shuo School of Computer Science and Technology Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230051 China Department of Computer Science and Technology Tsinghua University Beijing100084 China School of Engineering Case Western Reserve University Cleveland44106 United States

Few-shot learning alleviates the heavy dependence of medical image segmentation tasks on large-scale labeled data, but it shows a strong performance gap when dealing with new targets and new tasks compared with traditional deep learning. Existing few-shot medical image segmentation models mainly realize segmentation by learning the semantic class knowledge of a few known (support) samples and extending it to unknown (query) samples. However, the neglect of the differences between known and unknown samples leads to a certain deviation in the transfer of class knowledge, and the trained model tends to overfit the class knowledge of training classes, which greatly reduces the segmentation and generalization performance of few-shot model. In this work, we propose an interactive prototype learning and self-learning network to alleviate the above problems. Specifically, we propose an interactive prototype learning network to promote feature consistency between support images and query images to improve the accuracy of prototype metric learning. In addition, to avoid overfitting the training class knowledge and reduce the over-dependence of query segmentation on the class knowledge, we introduce a query features-guided self-learning network to achieve the finer segmentation of query images through its rich feature information. Our model obtained the competitive segmentation results on three multi-organ and multi-modal public datasets that show a substantial improvement in generalization ability. © 2024, The Authors. All rights reserved.

关键词： Zero-shot learning

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Tracing Human Stress from Physiological Signals using UWB Radar

arXiv

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arXiv 2024年

作者： Xu, Jia Xiao, Teng Lv, Pin Chen, Zhe Cai, Chao Zhang, Yang Xiong, Zehui Guangzhou University Guangzhou China Guangxi University Nanning China Intelligent Networking and Computing Research Center The School of Computer Science Fudan University Shanghai China Huazhong University of Science and Technology Wuhan China Nanjing University of Aeronautics and Astronautics Nanjing China Singapore University of Technology and Design Singapore

Stress tracing is an important research domain that supports many applications, such as health care and stress management;and its closest related works are derived from stress detection. However, these existing works cannot well address two important challenges facing stress detection. First, most of these studies involve asking users to wear physiological sensors to detect their stress states, which has a negative impact on the user experience. Second, these studies have failed to effectively utilize multimodal physiological signals, which results in less satisfactory detection results. This paper formally defines the stress tracing problem, which emphasizes the continuous detection of human stress states. A novel deep stress tracing method, named DST, is presented. Note that DST proposes tracing human stress based on physiological signals collected by a noncontact ultra-wideband radar, which is more friendly to users when collecting their physiological signals. In DST, a signal extraction module is carefully designed at first to robustly extract multimodal physiological signals from the raw RF data of the radar, even in the presence of body movement. Afterward, a multimodal fusion module is proposed in DST to ensure that the extracted multimodal physiological signals can be effectively fused and utilized. Extensive experiments are conducted on three real-world datasets, including one self-collected dataset and two publicity datasets. Experimental results show that the proposed DST method significantly outperforms all the baselines in terms of tracing human stress states. On average, DST averagely provides a 6.31% increase in detection accuracy on all datasets, compared with the best baselines. Copyright © 2024, The Authors. All rights reserved.

关键词： Physiological models

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Multi-Instance Active Learning for Relation Extraction

Multi-Instance Active Learning for Relation Extraction

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Meilin Cao Jiqiong Jiang Jing Zhang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Research Center of Intelligent Computing Software Zhejiang Lab Hangzhou China

Achieving high-accuracy relation extraction usually requires a large well-annotated corpus for model training, which is extremely time-consuming and labor-intensive. Consequently, distant supervision, implemented as a multi-instance learning paradigm, was proposed to build models from bags using automated labeled corpora, where massive noises are unavoidably introduced in the bag labels, deteriorating the model performance. To deal with the above problem, this paper proposes novel multi-instance active learning for relation extraction, which aims to improve the classification performance of the learned models under the premise of using as few labeled training samples as possible. The proposed active learning strategies for bag selection are based on the similarity between bags, the improved Fisher matrix, and the combination of the two (hybrid), respectively. Extensive experiment results on two real-world datasets under three base learners consistently show that our proposed active learning methods archive better performance for relation extraction, compared with three baseline methods. Furthermore, the hybrid bag selection strategy performs the best.

关键词： Training Learning systems

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SCR-Auth: Secure Call Receiver Authentication on Smartphones Using Outer Ear Echoes

arXiv

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arXiv 2024年

作者： Sun, Xiping Chen, Jing He, Kun He, Zhixiang Du, Ruiying Feng, Yebo Zhao, Qingchuan Wu, Cong Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan430072 China Rizhao Institute of Information Technology Wuhan University Rizhao276800 China Collaborative Innovation Center of Geospatial Technology Wuhan430079 China School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computer Science City University of Hong Kong Hong Kong

Receiving calls is one of the most universal functions of smartphones, involving sensitive information and critical operations. Unfortunately, to prioritize convenience, the current call receiving process bypasses smartphone authentication mechanisms (e.g., passwords, fingerprint recognition, and face recognition), leaving a significant security gap. To address this issue, we propose SCR-Auth, a secure call receiver authentication scheme for smartphones that leverages outer ear echoes. It sends inaudible acoustic signals through the earpiece speaker to actively sense the call receiver’s outer ear structure and records the resulting echoes using the top microphone. These echoes are then analyzed to extract unique outer ear biometric information for authentication. It operates implicitly, without requiring extra hardware or imposing additional burden. Comprehensive experiments conducted under diverse conditions demonstrate SCR-Auth’s effectiveness and security, showing an average balanced accuracy of 96.95% and resilience against potential attacks. Copyright © 2024, The Authors. All rights reserved.

关键词： Smartphones

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Diffusion Model with Self-supervised Training for Subject-aware Photoplethysmography Recovery

Diffusion Model with Self-supervised Training for Subject-aw...

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IEEE Biomedical Circuits and Systems (BIOCAS)

作者： Daomiao Wang Qihan Hu Xujian Feng Cuiwei Yang Center for Biomedical Engineering School of Information Science and Technology Fudan University Shanghai China Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Shanghai China

Photoplethysmography (PPG) has emerged as a promising technology for wearable personal healthcare due to its non-invasive and cost-effective nature. However, owing to the vulnerability to various noise, the useful PPG signals are few and the usability of PPG-based systems are limited. In this paper, we address the challenge of recovering extremely corrupted PPG waveforms with severe distortions, which lead to missing data. Specifically, we propose a novel diffusion-based framework that incorporates remaining values and subject-aware embedding, along with a self-supervised generative training strategy. Extensive experiments were performed on two datasets under three missing rates and two missing patterns. Our proposed model achieves significant improvements of nearly 100% over existing imputation models on quantitative metrics, and together with the visualization, demonstrating its effectiveness for data distribution generation and corrupted PPG restoration. Our proposed method also provides a new perspective for PPG denoising.

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Learning from Crowds with Mutual Correction-Based Co-Training

Learning from Crowds with Mutual Correction-Based Co-Trainin...

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IEEE International Conference on Big Knowledge (ICBK)

作者： Nianshang Tian Ming Wu Jiqiong Jiang Jing Zhang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Zhejiang Lab Research Center of Intelligent Computing Software Hangzhou China

Collecting labels from crowds is a feasible solution for large knowledge graph creation. However, errors in crowd-sourced labels damage the predictive model training. Although various methods have been developed to correct noisy labels, these traditional methods on the one hand do not take full advantage of the information from crowdsourced labels, and on the other hand do not perform well facing complicated annotation conditions, especially when some crowd workers make highly correlated mistakes. To address this issue, this paper proposes a novel learning from crowds method, which can simultaneously correct label noises and learn predictive models. The proposed method trains two data classifiers and a crowd aggregator. Each data classifier divides crowdsourced labeled data into a clean set and a noisy set and predicts the labels of the instances with a large loss in the noisy set. These instances are removed from the noisy set and put into the clean set. Then, the loss of the classifier is updated using the new clean and noisy sets. The data classifier with smaller loss is selected and trained with the crowd aggregator by maximizing their mutual information. Simultaneously, the smaller loss is back propagated to the other data classifier for training it. Experimental results on both synthetic and real-world datasets consistently show that the proposed method significantly outperforms the state-of-the-art methods in noise correction and predictive model learning.

关键词： Training Crowdsourcing Annotations Predictive models Propagation losses Data models Noise measurement

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Unlocking Textual and Visual Wisdom: Open-Vocabulary 3D Object Detection Enhanced by Comprehensive Guidance from Text and Image

arXiv

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arXiv 2024年

作者： Jiao, Pengkun Zhao, Na Chen, Jingjing Jiang, Yu-Gang Shanghai Key Lab of Intell. Info. Processing School of Computer Science Fudan University China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China Singapore University of Technology and Design Singapore

Open-vocabulary 3D object detection (OV-3DDet) aims to localize and recognize both seen and previously unseen object categories within any new 3D scene. While language and vision foundation models have achieved success in handling various open-vocabulary tasks with abundant training data, OV-3DDet faces a significant challenge due to the limited availability of training data. Although some pioneering efforts have integrated vision-language models (VLM) knowledge into OV-3DDet learning, the full potential of these foundational models has yet to be fully exploited. In this paper, we unlock the textual and visual wisdom to tackle the open-vocabulary 3D detection task by leveraging the language and vision foundation models. We leverage a vision foundation model to provide image-wise guidance for discovering novel classes in 3D scenes. Specifically, we utilize a object detection vision foundation model to enable the zero-shot discovery of objects in images, which serves as the initial seeds and filtering guidance to identify novel 3D objects. Additionally, to align the 3D space with the powerful vision-language space, we introduce a hierarchical alignment approach, where the 3D feature space is aligned with the vision-language feature space using a pre-trained VLM at the instance, category, and scene levels. Through extensive experimentation, we demonstrate significant improvements in accuracy and generalization, highlighting the potential of foundation models in advancing open-vocabulary 3D object detection in real-world scenarios. © 2024, CC BY.

关键词： Object detection

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Design and Implementation of Business Intelligence Framework for a Global Online Retail Business

Design and Implementation of Business Intelligence Framework...

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2022 International Conference on Emerging Trends in computing and Engineering Applications, ETCEA 2022

作者： Al-Omoush, Razan Fraihat, Salam Al-Naymat, Ghazi Awad, Mohammed Princess Sumaya University for Technology King Hussein School of Computing Sciences Amman Jordan Ajman University Artificial Intelligence Research Center College of Engineering and Information Technology United Arab Emirates American University of Ras Al Department of Computer Science and Engineering Khaimah United Arab Emirates

ISBN: (纸本)9781665477093

Due to the intense competition in today's online retail environment, companies seek to enhance their strategies by adopting effective analytical techniques and infrastructure, allowing them to quickly analyze critical information that supports decision-making. A Business intelligence (BI) framework can promptly fulfill such needs by processing massive amounts of collected data from multiple sources and representing them in a way companies can utilize in their strategic decisions. This research paper presents a detailed design and implementation of a BI framework for the online retail business industry. It includes requirement analysis, data modeling, BI framework design, and the implementation of descriptive and predictive analytic tools to provide insights and decision support for retail businesses. Moreover, the paper details the implementation of various machine learning algorithms used in sales predictive analytics, such as Linear Regression, Lasso Regression, XGBoost, Random Forest, and LSTM. Interactive charts are provided to assist decision-makers in carrying informed decisions. © 2022 IEEE.

关键词： Predictive analytics

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FlashWalker: An In-Storage Accelerator for Graph Random Walks

FlashWalker: An In-Storage Accelerator for Graph Random Walk...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Fuping Niu Jianhui Yue Jiangqiu Shen Xiaofei Liao Haikun Liu Hai Jin National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Computer Science Michigan Technological University Houghton Michigan USA

Graph random walk is widely used in the graph processing as it is a fundamental component in graph analysis, ranging from vertices ranking to the graph embedding. Different from traditional graph processing workload, random walk features massive processing parallelisms and poor graph data reuse, being limited by low I/O efficiency. Prior designs for random walk mitigate slow I/O operations. However, the state-of-the-art random walk processing systems are bounded by slow disk I/O bandwidth, which is confirmed by our experiments with real-world graphs. To address this issue, we propose FlashWalker, an in-storage accelerator for random walk that moves walk updating close to graph data stored in flash memory, by exploiting significant parallelisms inside SSD. Featuring a heterogeneous and parallel processing system, FlashWalker includes a board-level accelerator, channel-level accelerators, and chip-level accelerators. To address challenges posed by the tight resource constraints for processing large-scale graphs, we propose novel designs: storing a few popular subgraphs in accelerators, the pre-walking for dense walks, two optimizations to search the subgraph mapping table, and a subgraph scheduling algorithm. We implement FlashWalker in RTL, showing small circuit area overhead. Our evaluation shows FlashWalker reduces the execution time of random walk algorithms by up to 660.50×, compared with GraphWalker, which is the state-of-the-art system for random walk algorithms.

关键词： Distributed processing Scheduling algorithms Bandwidth Parallel processing Distance measurement Flash memories Optimization

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