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International Conference on computer Supported Cooperative Work in Design

作者： Xiaowei Liu Wenhui Chen Mengmeng Sun Yali Si Zhen Chen School of Information Science and Engineering Yanshan University Qinhuangdao China School of Computer Science and Engineering Changshu Institute of Technology Changshu China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

With the popularity of the service-oriented development paradigm in the cloud era, numerous cloud APIs have been developed. The continuous proliferation of cloud APIs has submerged developers in a sea of options, bringing the need for cloud API recommendation techniques. Most existing cloud API recommendation methods focus on theoretical studies and neglect practical applications. As a result, these methods face several challenges, such as covering recommendation scenarios, generating real-time responses, and handling concurrent requests. In this paper, we propose and implement a practical cloud API complementary recommendation service for mashup creation. To address the above challenges, we first propose a cloud API complementary recommendation framework named FICR, which exploits feature interactions to mine implicit complementarity and models complementarity from the mashup side and the target cloud API side for scenario coverage. Then, we conduct performance optimization via precoding and batch size tuning to optimize FICR for real-time responses. Finally, we design a minimum-connection load balancing algorithm with resource early warning and build a distributed system for concurrent requests. Experiments conducted on real-world datasets show that FICR outperforms state-of-the-art baseline models in complementary recommendation scenarios, and demonstrate that the implemented service meets the real-time and concurrency requirements. The service will serve as a practical tool to help developers during mashup creation.

关键词： Concurrent computing Federated learning Mashups Precoding Load management Real-time systems Tuning

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HCU-Net: An Efficient Fully Convolutional Neural Network for Thyroid Nodules Ultrasound Image Segmentation

HCU-Net: An Efficient Fully Convolutional Neural Network for...

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IEEE International Conference on systems, Man and Cybernetics

作者： Mengxi Ren Jun Liu School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Thyroid nodules, which can manifest as solitary or multiple growths, are among the most prevalent diseases with a high incidence rate. When examining thyroid nodules, ultrasound is typically the first step. Ultrasound image segmentation of thyroid nodules can assist physicians in diagnosis. However, the following three factors limit the development of thyroid nodule segmentation: (1) most of the existing studies optimize the segmentation of single nodules, and the incidence of multiple nodules is higher, so the segmentation of multiple nodules is also very important; (2) the ultrasound image of thyroid nodules is difficult to segment because of its low resolution and fuzzy boundary; (3) Due to the small data set of thyroid nodule ultrasound, in such a small dataset, attention mechanisms may be prone to overfitting, resulting in poorer generalization performance. Therefore, the attention-based model may not be suitable for the segmentation of thyroid nodules. Hence, we propose Hybrid Convolutional U-Net (HCU-Net), a simple and strong fully convolutional neural network for the segmentation of single or multiple thyroid nodules. HCU-Net incorporates a large receptive field to ensure accurate and stable segmentation. Specifically, we design Hybrid Branch Block to enhance the network's feature extraction capability for single or multiple nodules in ultrasound images by integrating multi-scale features. Furthermore, we add Enhanced ConvNeXt Blocks to the U-Net's final layer, enabling the extraction of global contextual information for segmentation performance. The Experimental results show that our proposed method can effectively improve these issues on two publicly available datasets. In addition, compared to attention-based models, our network is simpler and more suitable for small datasets like ultrasound images.

关键词： Image segmentation Ultrasonic imaging Image resolution Medical services Feature extraction Convolutional neural networks Medical diagnostic imaging Thyroid Overfitting Diseases

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Multi-Scale and Improved Skip Connections Network for Skin Lesion Segmentation

Multi-Scale and Improved Skip Connections Network for Skin L...

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6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Botao Zhang Jun Chen College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

To solve the problem that the current skin lesion segmentation networks have a poor segmentation accuracy when lesion areas are of different shapes and sizes and low contrast at the edge areas, based on U-Net, we present a skin lesion segmentation network based on multi-scale and improved skip connections. Firstly, a multi-scale feature enhancement module is designed to replace the U-Net's $\boldsymbol{3\times 3}$ convolution blocks, which can improve the feature extraction ability of the network about lesion areas of different shapes and sizes. Secondly, a lesion attention module is introduced into the skip connection to improve the semantic expression of low-level features and the location attention of high-level features, which can improve the fine-grained information transmission efficiency at the skip connection. Finally, a new hybrid loss function is designed to calculate the label and the edge loss of lesion areas, which improves the ability of the network to segment lesion areas and edges. Experiments are conducted on the ISIC2017 and ISIC2018 datasets, and the results prove that our method have a higher segmentation accuracy in skin lesion segmentation.

关键词： Accuracy Shape Convolution Semantics information processing Feature extraction Skin Lesions

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Digital forensics for the socio-cyber world (DF-SCW): A novel framework for deepfake multimedia investigation on social media platforms

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Egyptian Informatics Journal 2024年 27卷

作者： Ayub Khan, Abdullah Chen, Yen-Lin Hajjej, Fahima Ahmed Shaikh, Aftab Yang, Jing Soon Ku, Chin Yee Por, Lip Department of Computer Science and Information Technology Benazir Bhutto Shaheed University Lyari Sindh Karachi75660 Pakistan Department of Computer Science Sindh Madressatul Islam University Sindh Karachi74000 Pakistan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei106344 Taiwan Department of Software Engineering College of Computer and Inf. Sci. Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia Department of Computer Science Universiti Tunku Abdul Rahman Kampar31900 Malaysia

Owing to the major development of social media platforms, the usage of technological adaptation increases by means of editing software tools. Posting media in social communication environments has become one of our common daily routines. Before posting, various editing generators are used to manipulate pixel values, such as for enhancing brightness and contrast. Undoubtedly, this software helps bring posting media from ordinary to outstanding. But such a type of editing crosses the line in terms of creating fakes—anything that comes from anywhere and does not retain its originality anyway. It poses a series of issues in the process of multimedia forensics investigation and chain of custody. In order to restrict the attempts at deep faking and make the investigation hierarchy more effective, efficient, and reliable in the socio-cyber space (SCS), this paper presents a novel framework called DF-SCW. A digital forensics-enabled socio-cyber world with artificial intelligence (AI), especially deep neural networks (DNNs), for detecting and analyzing deep fake media investigations on social media platforms. It compares pixels with their neighboring values in the same media (such as images and videos) to identify information about the original one. There is a media flag designed to filter out malicious and dangerous attempts, like a powerful leader declaring war. Putting flags on such fakes helps digital investigators resist sharing the posts. In addition, the other prospect of this research is to make the digital forensics ecosystem more appropriate to take qualitative judgments in real-time while media is uploaded on social media platforms. The simulation of the proposed DF-SCW is tested on three different platforms, such as Instagram, Facebook, and Twitter. Through the experiment, the DF-SCW outperformed in terms of detection, identification, and analysis of deepfake media by an increased rate of 3.77%. © 2024

关键词： Social networking (online)

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On the Generalization and Causal Explanation in Self-Supervised Learning

arXiv

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arXiv 2024年

作者： Qiang, Wenwen Song, Zeen Gu, Ziyin Li, Jiangmeng Zheng, Changwen Sun, Fuchun Xiong, Hui The National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China The National Key Laboratory of Space Integrated Information System Department of Computer Science and Technology Tsinghua University Beijing China The Hong Kong University of Science and Technology China

Self-supervised learning (SSL) methods learn from unlabeled data and achieve high generalization performance on downstream tasks. However, they may also suffer from overfitting to their training data and lose the ability to adapt to new tasks. To investigate this phenomenon, we conduct experiments on various SSL methods and datasets and make two observations: (1) Overfitting occurs abruptly in later layers and epochs, while generalizing features are learned in early layers for all epochs;(2) Coding rate reduction can be used as an indicator to measure the degree of overfitting in SSL models. Based on these observations, we propose Undoing Memorization Mechanism (UMM), a plug-and-play method that mitigates overfitting of the pre-trained feature extractor by aligning the feature distributions of the early and the last layers to maximize the coding rate reduction of the last layer output. The learning process of UMM is a bi-level optimization process. We provide a causal analysis of UMM to explain how UMM can help the pre-trained feature extractor overcome overfitting and recover generalization. We also demonstrate that UMM significantly improves the generalization performance of SSL methods on various downstream tasks. The source code is to be released at https://***/ZeenSong/UMM. © 2024, CC BY.

关键词： Self-supervised learning

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Dt2f-Tlnet: A Novel Text-Independent Writer Identification Model Using a Combination of Deep Type-2 Fuzzy Architecture and Transfer Learning Networks Based on Handwriting Data

SSRN

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SSRN 2023年

作者： Yang, Jing Shokouhifar, Mohammad Yee, Por Lip Khan, Abdullah Ayub Awais, Muhammad Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia Department of Electrical & Computer Engineering Shahid Beheshti University Tehran*** Iran Department of Computer Science and Information Technology Benazir Bhutto Shaheed University Lyari Karachi75660 Pakistan COMSATS University Islamabad Pakistan

Identifying and verifying the identity of people based on scanned images of handwritten documents is an applicable biometric modality with applications in forensic and historic document investigation, and it is an important study area within the research field of behavioral biometrics. Despite this, there are few studies in this field. Furthermore, there are very few standard datasets for identifying and verify handwritten documents. Also, handwritten documents lose their character during time because of ink spread and drying. Therefore, it is necessary to provide a method that can identify and verify handwritten documents under various uncertainties. In this study, a text-independent writer identification and verification model in offline state under different experimental conditions is developed using a combination of Deep Type-2 Fuzzy architecture and Transfer Learning networks (DT2F-TLNet). So, a right-to-left dataset has been collected. The proposed DT2F-TLNet model is validated using both the designed dataset and other benchmark datasets. The proposed model is distinguished by the fact that it is developed to be independent of the textual content of the handwritten cases and can be used for various languages. The study's findings show that the developed DT2F-TLNet model can learn properties from heterogeneous handwriting data and results in higher accuracy than other comparable approaches. © 2023, The Authors. All rights reserved.

关键词： Biometrics

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Research and Application of Material Data for Digital Manufacturing

Research and Application of Material Data for Digital Manufa...

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Image Processing and computer Applications (ICIPCA), IEEE International Conference on

作者： Ganlong Wang Guoyin Zhang Jichang Wang Jianxun Chen Computer Science and Technology Harbin Engineering University CSIC Information Technology Co. Ltd. Beijing China Computer Science and Technology Harbin Engineering University Harbin China System General Department CSIC Information Technology Co. Ltd. Beijing China

The organization mode of “headquarters and multi-factories” has been widely developed in manufacturing enterprises. Although the multi-production base model meets the needs of rapid business expansion, it puts forward higher requirements for enterprise management. The differences in cost accounting methods, planning methods, and operation modes of the factories under the headquarters lead to different production management organizations, purchasing organizations, and sales organizations. Because the basic materials are managed by the headquarters, each factory can only select the required materials from the headquarters material warehouse. Therefore, there is a phenomenon that the same item corresponds to multiple business views. In this paper, the full attribute management mode of material data is taken as the research object, and a full attribute management solution with material code and factory code as the unique identification is proposed. It provides the theoretical basis and practical guidance for the application and promotion of material data life cycle management in multi-factory mode.

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Make 3D Cable Model from Single RGB Image 10

Make 3D Cable Model from Single RGB Image

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10th International Congress on Advanced Applied Informatics, IIAI-AAI 2021

作者： Fan, Zheming Ohashi, Takeshi Hayashi, Toyohiro Graduate School of Computer Science and System Engineering Kyushu Institute of Technology Fukuoka Japan Kyushu Institute of Technology Information Science and Technology Center Fukuoka Japan

ISBN: (纸本)9781665424202

Along with the electrification, there are many electrical equipment are used in our daily life. Using those equipment we need cables to charge and connect them. But, sometimes cables will become problems, like when we clean the room the cables will be annoying. We think if we can resolve this problem with robot, it will save lots of time for people. But, unfortunately, there is no system which can manipulate cables with robot. The key point is cable can deform it's shape. Because of this, we can't recognize and record cables position correctly from picture. And if we want to manipulate cables with robot, the 3D information of cables is far batter than 2D information. Therefore, in this paper, we made a system which can build a 3D voxels model base on 2D image as a beforehand for use robot to manipulate cables. In our system, we cut input image into a series of small pictures and make 3D model based on each pictures then we assemble those 3D model together. So we can get a complete 3D model(output of this system) which is made base on input image and can represent the 3D cables position. Our method can transfer 2D data to 3D data, this let us can get 3D information from image and we think this is useful in robot science. © 2021 IEEE.

关键词： Cables

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Identify the potential pathways and candidate biomarkers of stroke associated with bipolar disorder: Bioinformatics and system biology approach

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Journal of Proteins and Proteomics 2025年第2期16卷 141-159页

作者： Hosen, Md. Faruk Basar, Md. Abul Yasmin, Mst. Farjana Hasan, Md. Rakibul Uddin, Muhammad Shahin Department of Information and Communication Technology (ICT) Mawlana Bhashani Science and Technology University (MBSTU) Tangail Bangladesh Department of Computing and Information System (CIS) Daffodil International University (DIU) Dhaka Bangladesh Department of Computer Science and Engineering (CSE) Uttara University Dhaka Bangladesh Department of Software Engineering (SWE) Daffodil International University (DIU) Dhaka Bangladesh

The field of computational bioinformatics and systems biology analysis is rapidly expanding due to advancements in bioinformatics tools. Bipolar disorder (BD) is a severe psychiatric condition affecting both adults and adolescents. In recent years, the risk of stroke has increased among individuals with BD. Background studies indicate that BD and stroke share numerous biochemical and genetic characteristics. Due to traditional endocrinological approaches, no collaborative research exists, and no treatment options have been established for BD and stroke patients. The aim of this study is to identify common molecular pathways and potential therapeutic targets in BD and stroke that could be utilized to predict disease progression. To achieve this, shared genes were identified to determine common pathways. Based on the biochemical, molecular, and genetic interactions among these shared genes, this study identifies the most significant hub genes. To further explore these interactions, analysis such as the protein–protein interaction (PPI) network, topological properties, enrichment analysis, co-expression network, gene regulatory network (GRN), and physical interaction network are conducted. The GO annotation indicates that most of the genes are linked to MAP kinase activity and tumor necrosis factor receptor superfamily binding. The KEGG pathway analysis revealed that the predominant genes are associated with apoptosis and the IL-18 signaling pathway. This comparison helps understand the biochemical and genetic characteristics shared between BD and stroke. Predictive drug analysis has identified consistent potential biomarkers associated with both conditions, providing a scientific foundation for investigating their diagnosis, treatment, and prognosis. Finally, chemical experiments could be conducted to further validate the efficacy of these drugs.

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Complementarity Analysis of Cloud API Ecosystems: An Empirical Study on ProgrammableWeb and Huawei AppGallery Datasets

Complementarity Analysis of Cloud API Ecosystems: An Empiric...

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IEEE International Conference on Web Services (ICWS)

作者： Xiaowei Liu Mengmeng Sun Wenhui Chen Dianlong You Limin Shen Zhen Chen School of Information Science and Engineering Yanshan University Qinhuangdao China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao China

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

With the proliferation of the digital economy, cloud API has become the best carrier for data transmission and capability replication in software development. In the service-oriented software development paradigm, developers select appropriate cloud APIs for mashup creation based on their practical needs, where complementarity is an important consideration. Complementarity is reflected in various implicit relationships within mashups and cloud APIs, making complementarity analysis of cloud API ecosystems a critical issue. However, existing studies have overlooked the complementarity, and the lack of application variety in the primary dataset ProgrammableWeb has added a threat to the validity and generalization. In this paper, we first build a dataset of two cloud API ecosystems: ProgrammableWeb centering on mashup creation and Huawei AppGallery centering on mobile app development. Then, we conduct statistical and graph analysis to compare the two ecosystems. Finally, we propose a modeling approach to quantify complementarity and perform an analysis to investigate the role of complementarity in cloud API ecosystems. The results reveal differences between mashups and mobile apps in their preferences for cloud APIs. It also highlights the potential of complementarity in addressing the long-tail problem in cloud API ecosystems and improving cloud API recommendations.

关键词： Analytical models Mashups Ecosystems Mobile applications Data communication Software development management

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