检索结果-内蒙古大学图书馆

International Conference on computer Technology and Development (ICCTD)

作者： Zhang Juan Lizhi Cai Weiqing Tong Yuan Song Li Ying School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Key Laboratory of Computer Software Evaluating & Testing Shanghai China Jiangxi University of Finance and Economics Jiangxi China East China University of science and technology Shanghai China Nanjing XiaoZhuang University NanJing China

As organizations implement systematic reuse test cases in software testing to improve productivity and quality, they must be able to measure reusability of test case and identify the most effective reuse strategies. This is done with Test Case Reusability Metrics Models(TCRMM). In this article we survey metrics of test case reusability, and provide reusability factors division that will help establish reusability assessment model. It combines four metrics factors into reusability metrics based on analytic hierarchy process. The prominent characteristic of this model is that the process of reusability metric is automated when the judgment matrix has been constructed.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Joint Clustering and Analyze Single Cell Multi-omics Data by scMOGAN

Joint Clustering and Analyze Single Cell Multi-omics Data by...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Congcong An 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 SG Faculty of Engineering The University of Sydney Australia

Advancements in single-cell multi-omics sequencing technologies have dramatically transformed the analysis of cellular states at single-cell resolution. Cluster analyses leveraging single-cell transcriptome and epigenome data have enabled the characterization of cellular states and the delineation of transcriptomic regulatory programs associated with cellular heterogeneity. However, the high dimensionality, sparsity, and heterogeneity inherent to multi-omics data present significant challenges to their clustering analysis. In this study, we introduced single-cell Multi-Omics Generative Adversarial Networks (scMOGAN), an unsupervised clustering model specifically tailored for single-cell transcriptome and epigenome data. scMOGAN accomplishes this by efficaciously modeling the omics data and mining potential features through neural networks, ultimately utilizing Gaussian mixture models for cell type identification. To validate and scrutinize the performance of scMOGAN, we performed analyses on two actual multi-omics datasets and one simulated dataset. The results demonstrate that scMOGAN surpasses other existing methods in terms of clustering performance, underscoring its efficacy in navigating the complexities of multi-omics data.

关键词：

来源：评论

学校读者我要写书评

暂无评论

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

arXiv

引用

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

来源：评论

学校读者我要写书评

暂无评论

PMMVar: Leveraging Multi-level Protein Structures for Enhanced Coding Variant Pathogenicity Prediction

PMMVar: Leveraging Multi-level Protein Structures for Enhanc...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yijia Chen 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 Singapore Faculty of Engineering The University of Sydney Australia

Genomic variants, which can disrupt cellular functions, present a challenge in distinguishing deleterious from benign instances. While assessing genome-wide functional impacts, many current algorithms neglect protein tertiary structure of coding region variants due to limitations in protein structural prediction. This study introduces PMMVar, an advanced multimodal deep convolutional network, which adeptly integrates protein tertiary structures with conservation properties from ESM-2, supplemented by other protein structural sequences. PMMVar achieves outstanding performance on the latest clinical variant datasets, NCBI ClinVar (2023), and the Mendelian variant dataset, surpassing existing benchmarks. Ablation analyses validate the significance of protein multi-level structures in enhancing the model’s accuracy. Overall, our findings spotlight the essential role of multi-level protein structures in pathogenicity predictions and their potential to discern deleterious genomic variants effectively.

关键词：

来源：评论

学校读者我要写书评

暂无评论

SAMMS: Multi-modality Deep Learning with the Foundation Model for the Prediction of Cancer Patient Survival

SAMMS: Multi-modality Deep Learning with the Foundation Mode...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wen Zhu 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 Singapore Faculty of Engineering The University of Sydney Australia

Cancer survival prediction is pivotal in tailoring individualized treatment strategies and guiding clinician decision-making. Yet, existing methodologies grapple with efficiently harnessing the intricate distribution of medical data spanning various modalities. In response, we present SAMMS, an advanced multi-omics multimodal deep learning framework tailored for survival prediction. SAMMS leverages the robust image segmentation model, "Segment Anything" to adeptly characterize pathological images. This prowess is further enhanced by integrating multi-omics data and clinical insights, facilitating holistic modeling across a diverse modal spectrum. The framework weaves a modality-specific subnetwork with a cross-modality common subnetwork, meticulously capturing intra-modality nuances and inter-modality correlations. SAMMS eclipsed its contemporaries by delivering remarkable performance on TCGA’s LGG and KIRC tumor datasets. A battery of analyses underscored SAMMS’s unparalleled capability to distill multifaceted insights from multimodal datasets, yielding richer and more integrative multimodal representations. Such strides promise significant advancements in cancer survival analytics, bolstering the precision and efficacy of patient-centric treatments, disease oversight, and clinical decision processes.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Applying Probabilistic Model Checking to Service Discovery Framework

Applying Probabilistic Model Checking to Service Discovery F...

引用

IEEE International Conference on Services Computing (SCC)

作者： Honghao Gao Huaikou Miao Yucong Duan Jinyu Kai School of Computer Engineering and Science Shanghai University Shanghai P.R. China Shanghai Key Laboratory of Computer Software Evaluating&Testing Shanghai P.R. China College of Information Science and Technology Hainan University Haikou P.R. China

The complex requirements of E-commerce application call for selecting a set of Web services to reuse theirs business logics, where Service Oriented Architecture (SOA) provides a promising solution to the problem of cross-platform services integration. To this purpose, how to discovery services is a key to support the quick services composition, which has been a challenging task in Web application engineering. However, the traditional approaches have limitations in recall ratio and precision ratio because the keyword and semantic annotation query modes are hard to verify the target Web service, such as interaction behaviors and control flows of composite service. In this paper, it proposes a probabilistic model checking based service discovery method. First, it focuses on service reliability and time constraints to formalize the probabilistic behaviors of service process. Second, the quantitative verification properties of user requirements are specified in the form of temporal logic formulae. Third, the service discovery is to verify service process model against expected properties for identifying candidate services. Finally, in order to reduce model checking tasks, the correlation based service recommendation is introduced to explore more suitable services for user. Our framework improves the efficiency of service discovery without changing any existing service infrastructures.

关键词： Probabilistic logic Model checking Clocks Web services Business Time factors

来源：评论

学校读者我要写书评

暂无评论

Multi-Signal Fusion of Social Diffusion Graph with Bi-Directional Semantic Consistency

Multi-Signal Fusion of Social Diffusion Graph with Bi-Direct...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Huacheng Li Chunhe Xia Tianbo Wang Wanshuang Lin Changnan Jiang Chen Chen Yuan Zhao Key Laboratory of Beijing Network Technology Beihang University Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University School of Cyber Science and Technology Beihang University Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai

Devising diffusion graph to learn user representations is a crucial step in studying information propagation prediction. However, previous works mainly focused on structural and temporal features. To better incorporate content features, we introduce the Backward Decomposition and Forward Preservation mechanisms. The former involves decomposing content features for initializing node signals in the diffusion graph, thus fusing user features with content features. The latter aims to maintain node features generated by graph encoder consistent with the original content features. A series of experiments demonstrate that our model outperforms state-of-the-art models, and both mechanisms significantly enhance the prediction performance. Furthermore, our methods enables the features generated by the diffusion graph to more effectively incorporate features from various semantic spaces, whether encoded by language models or generated by graph embedding algorithms.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A universal multiple instance learning framework for whole slide image analysis

引用

computers in Biology and Medicine 2024年 178卷 108714-108714页

作者： Zhang, Xueqin Liu, Chang Zhu, Huitong Wang, Tianqi Du, Zunguo Ding, Weihong College of Information Science and Engineering East China University of Science and Technology Shanghai200237 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China Department of Pathology Huashan Hospital Affiliated to Fudan University Shanghai200040 China Department of Urology Huashan Hospital Affiliated to Fudan University Shanghai200040 China

Background: The emergence of digital whole slide image (WSI) has driven the development of computational pathology. However, obtaining patch-level annotations is challenging and time-consuming due to the high resolution of WSI, which limits the applicability of fully supervised methods. We aim to address the challenges related to patch-level annotations. Methods: We propose a universal framework for weakly supervised WSI analysis based on Multiple Instance Learning (MIL). To achieve effective aggregation of instance features, we design a feature aggregation module from multiple dimensions by considering feature distribution, instances correlation and instance-level evaluation. First, we implement instance-level standardization layer and deep projection unit to improve the separation of instances in the feature space. Then, a self-attention mechanism is employed to explore dependencies between instances. Additionally, an instance-level pseudo-label evaluation method is introduced to enhance the available information during the weak supervision process. Finally, a bag-level classifier is used to obtain preliminary WSI classification results. To achieve even more accurate WSI label predictions, we have designed a key instance selection module that strengthens the learning of local features for instances. Combining the results from both modules leads to an improvement in WSI prediction accuracy. Results: Experiments conducted on Camelyon16, TCGA-NSCLC, SICAPv2, PANDA and classical MIL benchmark datasets demonstrate that our proposed method achieves a competitive performance compared to some recent methods, with maximum improvement of 14.6 % in terms of classification accuracy. Conclusion: Our method can improve the classification accuracy of whole slide images in a weakly supervised way, and more accurately detect lesion areas. © 2024 Elsevier Ltd

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Instance and Category Supervision are Alternate Learners for Continual Learning

Instance and Category Supervision are Alternate Learners for...

引用

International Conference on computer Vision (ICCV)

作者： Xudong Tian Zhizhong Zhang Xin Tan Jun Liu Chengjie Wang Yanyun Qu Guannan Jiang Yuan Xie East China Normal University Shanghai Key Laboratory of Computer Software Testing & Evaluating Chongqing Institute of East China Normal University Tencent YouTu Lab Xiamen University Contemporary Amperex Technology Co. Limited

Continual Learning (CL) is the constant development of complex behaviors by building upon previously acquired skills. Yet, current CL algorithms tend to incur class-level forgetting as the label information is often quickly overwritten by new knowledge. This motivates attempts to mine instance-level discrimination by resorting to recent self-supervised learning (SSL) techniques. However, previous works have pointed out that the self-supervised learning objective is essentially a trade-off between invariance to distortion and preserving sample information, which seriously hinders the unleashing of instance-level *** this work, we reformulate SSL from the information-theoretic perspective by disentangling the goal of instance-level discrimination, and tackle the trade-off to promote compact representations with maximally preserved invariance to distortion. On this basis, we develop a novel alternate learning paradigm to enjoy the complementary merits of instance-level and category-level supervision, which yields improved robustness against forgetting and better adaptation to each task. To verify the proposed method, we conduct extensive experiments on four different benchmarks using both class-incremental and task-incremental settings, where the leap in performance and thorough ablation studies demonstrate the efficacy and efficiency of our modeling strategy.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Object Oriented BDD and Executable Human-Language Module Specification

Object Oriented BDD and Executable Human-Language Module Spe...

引用

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

作者： Eric Lee Jiayu Gong Qinghong Cao SE Cloud (Shanghai) Intelligent Technology Co. Ltd. Shanghai China Shanghai Key Laboratory of Computer Software Testing & Evaluation Shanghai Development Center of Computer Software Technology Shanghai China System Operation Department Shanghai Stock Exchange Shanghai China

This paper presents an approach to software development which uses a generative AI Model as compiler to translate human language requirements into high-level programming language. We propose an executable human-language module specification and a tool to support it, which has been used successfully for human-language UI test automation. We anticipate further development of this approach to enable complex software to be programmed in human language, allowing for more intuitive and efficient software development.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：