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International Conference on Big Data and Smart Computing (BIGCOMP)

作者： Hanbing Zhang Yinan Jing Wei Zhang Zhenying He Kai Zhang X. Sean Wang Zhenqiang Chen Changchun Zhang School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Data Science Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Transwarp Technology(Shanghai) Co. Ltd Shanghai China

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

Exploratory data analysis (EDA) is usually human-in-the-loop and time tedious since identifying insights that show interesting characteristics and trends in data is laborious. Insight discovery helps users gain structured knowledge when understanding the dataset by categorizing interesting characteristics and trends into commonnesses and exceptions. However, existing insight discovery methods cannot interact with users when discovering insights. More importantly, the efficiency of insight discovery for these methods is low. In this paper, we propose a framework that supports and accelerates interactive insight discovery. We first divide the dataset into multiple subsets, and precompute the pattern snippets that include the distribution characteristics of subsets. Considering efficiency, we only pre-compute for subsets that cannot be further split. Then we design a novel cube structure, called InsightCube, to store precomputed pattern snippets. In order to accelerate insight discovery, we reuse the precomputed pattern snippets stored in InsightCube to infer the pattern of subsets that cannot be split. Furthermore, during insight discovery, we propose pruning strategies to accelerate the pattern inference for subsets that can be split. Experimental results on real-world datasets show that InsightCube improves the insight discovery efficiency by up to $2.5 \times$ compared to the state-of-the-art insight discovery method.

关键词： Data analysis Big Data Market research Human in the loop Optimization

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Load Balancing Optimization for Transformer in Distributed Environment 27

Load Balancing Optimization for Transformer in Distributed E...

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27th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2021

作者： Ma, Delu Lei, Zhou Chen, Shengbo Wang, Peng School of Computer Engineering and Science Shanghai University Shanghai200444 China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai201112 China

ISBN: (纸本)9781665408783

In recent years, the demand for artificial intelligence applications has increased dramatically. Complex models can promote machine learning to achieve excellent results, but computing efficiency has gradually reached a bottleneck. Therefore, more researchers are exploring the improvement of the efficiency of intelligent computing systems. Distributed machine learning can improve the efficiency of model training and inference, but problems such as communication delay and load imbalance between computing nodes still exist. In the multi-GPU distributed computing environment, this paper takes the vision field algorithm VIT (vision transformer) as the optimization object, which has the advantage of convenient parallel training, and proposes several related solutions. Firstly, the parameter server is used as the system logic architecture and in order to reduce the idleness of the computing devices during the training process, the device working status query mechanism is designed to realize load balancing. Secondly, combined with the pre-trained small VIT algorithm model, semi-asynchronous communication method is proposed to reduce the communication overhead of computing devices and accelerate global convergence. The results of this experiment carried out in the existing distributed environment has demonstrated that compared with the existing synchronization method, the computational efficiency has been improved well under the premise of slightly reducing the accuracy. © 2021 IEEE.

关键词： Computation theory

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Location-Based Service Recommendation for Cold-Start in Mobile Edge Computing 17th

Location-Based Service Recommendation for Cold-Start in Mobi...

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17th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2020

作者： Yu, Mengshan Fan, Guisheng Yu, Huiqun Chen, Liang Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

ISBN: (纸本)9783030794774

With the rapid development of the 5G and Internet of Things (IoT), mobile edge computing has gained considerable popularity in academic and industrial field, which provides physical resources closer to end users. Service recommendation in such a distributed environment is a hot issue. However, the cold-start problem for service recommendation in mobile edge computing is still urgent to be solved. In this paper, we propose a service recommendation method based on collaborative filtering (CF) and user location, by comprehensively considering the characteristic of services at the edge and mobility of users. In detail, we first synthesize the service characteristics of each dimension through multidimensional weighting method. We further introduce the idea of Inverse_CF_Rec to the traditional CF and then predict the lost QoS value to solve the problem of sparse data. Finally, a recommendation algorithm based on predicted QoS value and user geographic location is proposed to recommend appropriate services to users. The experimental results show that our multidimensional inverse similarity recommendation algorithm based on collaborative filtering (MDICF) outperforms Inverse_CF_Rec in terms of the accuracy of recommendation. © 2021, IFIP International Federation for Information Processing.

关键词： Location based services

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Instance-Aware Domain Generalization for Face Anti-Spoofing

arXiv

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arXiv 2023年

作者： Zhou, Qianyu Zhang, Ke-Yue Yao, Taiping Lu, Xuequan Yi, Ran Ding, Shouhong Ma, Lizhuang Shanghai Jiao Tong University China Youtu Lab Tencent China Shanghai Key Laboratory of Computer Software Evaluating and Testing China Deakin University Australia

Face anti-spoofing (FAS) based on domain generalization (DG) has been recently studied to improve the generalization on unseen scenarios. Previous methods typically rely on domain labels to align the distribution of each domain for learning domain-invariant representations. However, artificial domain labels are coarse-grained and subjective, which cannot reflect real domain distributions accurately. Besides, such domain-aware methods focus on domain-level alignment, which is not fine-grained enough to ensure that learned representations are insensitive to domain styles. To address these issues, we propose a novel perspective for DG FAS that aligns features on the instance level without the need for domain labels. Specifically, Instance-Aware Domain Generalization framework is proposed to learn the generalizable feature by weakening the features' sensitivity to instance-specific styles. Concretely, we propose Asymmetric Instance Adaptive Whitening to adaptively eliminate the style-sensitive feature correlation, boosting the generalization. Moreover, Dynamic Kernel Generator and Categorical Style Assembly are proposed to first extract the instance-specific features and then generate the style-diversified features with large style shifts, respectively, further facilitating the learning of style-insensitive features. Extensive experiments and analysis demonstrate the superiority of our method over state-of-the-art competitors. Code will be publicly available at this link. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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LDE-UNet: A Novel Model for Rapid COVID-19 Diagnosis via CT Image Segmentation

LDE-UNet: A Novel Model for Rapid COVID-19 Diagnosis via CT ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： QiSong Wang JiaRou Wu YingZhuo Wang HuiLi Qu Shanling Nie Hai Yang Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Faculty of Engineering The University of Sydney Australia

The COVID-19 pandemic has had a profound impact on human society. It has highlighted the need for faster diagnostic methods. Research has shown that combining semantic segmentation with traditional medical approaches can significantly accelerate the process. To address this, leveraging COVID-19 CT images, our team designs a revolutionary semantic segmentation model called Level of Detail Enhancement U-Net (LDE-UNet), which shows the lesion area on CT images. By introducing the LDE block, the model has the unique advantage of overcoming the loss of data details during the downsampling process by emphasizing and transmitting details at the same level. Our SOTA model outperforms the second-best model by at least 0.7% in the most critical indicator precision. Compared with other models, LDE-UNet’s strong reliability determines its ability to be used in the medical field to accelerate the localization and division of lesion areas on CT images by professional doctors, thus completing patient diagnosis faster. In addition, we also propose a standardized method for processing medical images.

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Honeytoken-Detector: A Symbolic Execution-Based Honeypot Token Detection Tool

Honeytoken-Detector: A Symbolic Execution-Based Honeypot Tok...

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software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD-Winter), ACIS International Winter Conference on

作者： Yi Liu Lizhi Cai School of Information Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Testing & Evaluating Shanghai Development Center of Computer Software Technology Shanghai China

ISBN: (数字)9798350345865

ISBN: (纸本)9798350345872

Smart contracts have emerged as one of the most successful applications in the blockchain domain, playing a significant role in various blockchain ecosystems. Inspired by smart contracts, a multitude of cryptographic assets have been created. To standardize these assets, industry standards such as ERC20 (Ethereum Request for Comments 20), ERC721, and ERC1155 have been proposed. In recent years, smart contracts have frequently fallen victim to attacks. Honeypot contracts, disguised as ERC20-compliant tokens, are widely prevalent on the blockchain, enticing victims to make purchases. Such malicious smart contracts exhibiting deceptive behavior are collectively referred to as honeypot tokens. This paper focuses on ERC20-compliant smart contracts and defines six common types of honeypot issues. Building upon advancements in smart contract vulnerability detection, we propose an enhanced symbolic execution-based detection tool called Honeytoken-Detector. We conduct experiments on both contracts known to have similar issues and actual token contracts from the real world. The experimental results demonstrate the effectiveness of our tool in identifying vulnerabilities.

关键词： Industries Source coding Smart contracts Ecosystems Buildings Blockchains Behavioral sciences

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Modif-SegUnet: Innovatively Advancing Liver Cancer Diagnosis and Treatment through Efficient and Meaningful Segmentation of 3D Medical Images

Modif-SegUnet: Innovatively Advancing Liver Cancer Diagnosis...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Chenhang Wang Yujian Jin Jiayi Liang YuHan Yang Shanling Nie Yang Hai Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Faculty of Engineering The University of Sydney Sydney Australia

Hepatocellular Carcinoma (HCC) holds a record of high incidence and severe global harm. In tasks of liver cancer segmentation based on 3D medical images, the majority of methods have endeavored to enhance the 3D U-net by integrating the latest modules from the field of computer Vision (such as the transformer), often overlooking the distinct characteristics of liver components. We introduced a novel deep learning architecture, Modif-SegUnet, to circumvent this limitation. This architecture extends the U-net model by incorporating a 3D-modifiable attention module, thereby fostering a heightened focus on distinguishing between normal liver sections and lesions. Furthermore, Modif-SegUnet ingeniously amalgamates the 3D-modifiable attention module with the Modif-transformer block, enabling efficient capture of relevant and valuable full-text information in CT images that contain liver or tumor regions. We subjected the proposed Modif-SegUnet to evaluation on the Liver Tumor Segmentation benchmark dataset. Experimental outcomes indicate that our methodology surpasses state-of-the-art approaches in liver tumor segmentation, suggesting potential pathways for advancing the diagnosis and treatment of liver cancer.

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Learning Commonality, Divergence and Variety for Unsupervised Visible-Infrared Person Re-identification

arXiv

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arXiv 2024年

作者： Shi, Jiangming Yin, Xiangbo Zhang, Yachao Zhang, Zhizhong Xie, Yuan Qu, Yanyun Institute of Artificial Intelligence Xiamen University China School of Informatics Xiamen University China Shanghai Innovation Institute China East China Normal University China Shanghai Key Laboratory of Computer Software Evaluating and Testing China

Unsupervised visible-infrared person re-identification (USVI-ReID) aims to match specified people in infrared images to visible images without annotations, and vice versa. USVI-ReID is a challenging yet under-explored task. Most existing methods address the USVI-ReID using cluster-based contrastive learning, which simply employs the cluster center as a representation of a person. However, the cluster center primarily focuses on commonality, overlooking divergence and variety. To address the problem, we propose a Progressive Contrastive Learning with Hard and Dynamic Prototypes method for USVI-ReID. In brief, we generate the hard prototype by selecting the sample with the maximum distance from the cluster center. We theoretically show that the hard prototype is used in the contrastive loss to emphasize divergence. Additionally, instead of rigidly aligning query images to a specific prototype, we generate the dynamic prototype by randomly picking samples within a cluster. The dynamic prototype is used to encourage the variety. Finally, we introduce a progressive learning strategy to gradually shift the model’s attention towards divergence and variety, avoiding cluster deterioration. Extensive experiments conducted on the publicly available SYSU-MM01 and RegDB datasets validate the effectiveness of the proposed method. © 2024, CC BY.

关键词： Contrastive Learning

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CIXG: A Comprehensive Approach to Driver Gene Identification and Causal Interpretation

CIXG: A Comprehensive Approach to Driver Gene Identification...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yawen Liu Yiwen Chen Shanling Nie Hai Yang Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Center for Continuing and Lifelong Education National University of Singapore Faculty of Engineering The University of Sydney Australia

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

With the ongoing advancements in science and technology and the increasing research focus on cancer-related issues, there has been a proliferation of omics-related resources for in-depth analysis and exploration. This burgeoning volume and complexity of biological data have fostered the integration of machine-learning techniques into biology. As a result, numerous machine-learning strategies have been established to identify driver mutations. Yet, many of these strategies produce complex models, complicating comprehension and thereby clouding the impact of input features on the resulting predictions. Our analysis presented the CIXG framework, which integrates a driver gene prediction module using XGBoost with a causality interpretation module anchored on CXPlain. This architecture enables quantifying each input feature’s contribution to the prediction outcome and ensures precise predictions of driver genes. When benchmarked against the state-of-the-art (SOTA) method, CIXG demonstrated superior accuracy in pinpointing driver genes across pan-cancer studies and within the 32 specific cancer types. Importantly, our results underscored that mutation features chiefly influence CIXG’s predictive prowess, with additional support from other omics features.

关键词： Accuracy Biological system modeling Cause effect analysis Machine learning Predictive models Benchmark testing Complexity theory Bioinformatics Cancer

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Message from program chairs

Proceedings - 20th IEEE/ACIS International Summer Conference...

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Proceedings - 20th IEEE/ACIS International Summer Conference on computer and Information Science, ICIS 2021-Summer 2021年 X页

作者： Gong, Jiayu Shanghai Key Laboratory of Computer Software Testing and Evaluating China

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