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30th International Conference on Computational and Experimental engineering and Sciences, ICCES 2024

作者： Xu, Meng Wang, Jinwei State Key Laboratory of Mathematical Engineering and Advanced Computing Henan450001 China Department of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (纸本)9783031687747

Most of the images on the Internet are color images, and steganalysis of color images is a very critical issue in the field of steganalysis. The current proposed color image steganalysis features mainly rely on manual design, and the steganalysis features do not fully consider the internal connections between the three channels of color images. Most steganalyzers based on deep learning are also designed for grayscale images. In recent years, the continuous introduction of advanced steganography techniques for color images has also brought more severe challenges to the steganography analysis of color images. Quaternions are a tool that can effectively represent color images, and utilizing quaternion transformations can fully exploit the correlation between color image channels. Combining quaternion theory with convolutional neural networks, this paper proposed a color image steganalysis algorithm based on quaternion convolutional neural networks. Firstly, the image is represented by quaternion, and then input into the quaternion convolutional neural network. The convolution kernel in the network is composed of quaternions, and then perform convolution and pooling. The experimental results have demonstrated the significant effectiveness of the proposed network. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks

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Assistant Solutions in software engineering: A Systematic Literature Review 14

Assistant Solutions in Software Engineering: A Systematic Li...

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14th IEEE International Conference on software engineering and Service Science, ICSESS 2023

作者： Del Carpio, Alvaro Fernandez Angarita, Leonardo Bermon Universidad la Salle Department of Software Engineering Arequipa Peru Universidad Nacional de Colombia Computing Department Caldas Manizales Colombia

ISBN: (纸本)9798350336269

Increasing technological advancements in developing assistants have gained attention because of its wide use in a variety of fields. In the realm of software engineering, developers are including these solutions in diverse software tasks. This research aims to identify and analyze the role and diversity of these solutions, including forms of interaction, functionalities, and technologies used for their implementation, evaluation, drawbacks, and current challenges. This research involves a systematic literature review of different approaches of assistant solutions in order to support software practitioners in developing their tasks. The solutions identified are oriented to recommendation systems, chatbots, and virtual assistants, covering a wide range of software processes such as requirements, design, construction, maintenance and, additionally management and process learning. © 2023 IEEE.

关键词： software engineering

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Segmentation Based Approach For Off-line Handwritten Sinhala Word Recognition From Touch Screen Gestures 8

Segmentation Based Approach For Off-line Handwritten Sinhala...

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8th International Multidisciplinary Moratuwa engineering Research Conference, MERCon 2022

作者： Mahesh, Hashan Priyankara, Chathura University of Kelaniya Faculty of Computing and Technology Department of Software Engineering Kelaniya Sri Lanka

ISBN: (数字)9781665487863

ISBN: (纸本)9781665487863

The traditional way of using pen and paper to take notes is getting over by the touch screen devices. These devices provide more options to the users to enhance their productivity while taking notes. The ability to recognize and validate the words written on the touch screens facilitates further capabilities to the users. Hence, in this paper, we describe a segmentation-based approach combined with an n-gram model for the recognition and validation of the Sinhala words written on touch screens. We compare the results of 6 commonly used machine learning models to find the best performing classifier for recognizing individual characters of words. The classifiers are trained to identify 19 different Sinhala characters. Based on the results, Convolution Neural Network (CNN) based word classifier stands ahead of other classifiers. © 2022 IEEE.

关键词： Touch screens

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LPT-One and BFD-One Search Algorithms for Load Balance and Bin-Packing of Cloud computing 9

LPT-One and BFD-One Search Algorithms for Load Balance and B...

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9th IEEE International Conference on Cloud computing and Intelligence Systems, CCIS 2023

作者： Zhou, Guangyao Tian, Wenhong Buyya, Rajkumar University of Electronic Science and Technology of China School of Information and Software Engineering China The University of Melbourne School of Computing and Information Systems Australia

ISBN: (纸本)9798350304428

Cloud computing contains large-scale tasks and resources. Currently, the local search is a considerable choice in ensuring both computational complexity and optimization. Based on our previous research on multi-route search algorithm to reduce makespan, we apply BFDO and LPTO algorithms to address load balancing and bin-packing problems. Through abundant experiments, we validate the superiority of our proposed BFDO and LPTO. © 2023 IEEE.

关键词： Cloud computing

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MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis 41

MH-pFLID: Model Heterogeneous personalized Federated Learnin...

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41st International Conference on Machine Learning, ICML 2024

作者： Xie, Luyuan Lin, Manqing Luan, Tianyu Li, Cong Fang, Yuejian Shen, Qingni Wu, Zhonghai School of Software and Microelectronics Peking University Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China PKU-OCTA Laboratory for Blockchain and Privacy Computing Peking University Beijing China State University of New York BuffaloNY United States

Federated learning is widely used in medical applications for training global models without needing local data access. However, varying computational capabilities and network architectures (system heterogeneity), across clients pose significant challenges in effectively aggregating information from non-independently and identically distributed (non-IID) data. Current federated learning methods using knowledge distillation require public datasets, raising privacy and data collection issues. Additionally, these datasets require additional local computing and storage resources, which is a burden for medical institutions with limited hardware conditions. In this paper, we introduce a novel federated learning paradigm, named Model Heterogeneous personalized Federated Learning via Injection and Distillation (MH-pFLID). Our framework leverages a lightweight messenger model that carries concentrated information to collect the information from each client. We also develop a set of receiver and transmitter modules to receive and send information from the messenger model, so that the information could be injected and distilled with efficiency. Our framework eliminates the need for public datasets and efficiently share information among clients. Our experiments on various medical tasks including image classification, image segmentation, and time-series classification, show MH-pFLID outperforms state-of-the-art methods in all these areas and has good generalizability. Copyright 2024 by the author(s)

关键词： Federated learning

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A Comparative Analysis Between SciTokens, Verifiable Credentials, and Smart Contracts: Novel Approaches for Authentication and Secure Access to Scientific Data 23

A Comparative Analysis Between SciTokens, Verifiable Credent...

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2023 Practice and Experience in Advanced Research computing, PEARC 2023

作者： Hossain Faruk, Md Jobair Saha, Bilash Basney, Jim College of Computing and Software Engineering Kennesaw State University United States National Center for Supercomputing Applications University of Illinois United States

ISBN: (纸本)9781450399852

Managing and exchanging sensitive information securely is a paramount concern for the scientific and cybersecurity community. The increasing reliance on computing workflows and digital data transactions requires ensuring that sensitive information is protected from unauthorized access, tampering, or misuse. This research paper presents a comparative analysis of three novel approaches for authenticating and securing access to scientific data: SciTokens, Verifiable Credentials, and Smart Contracts. The aim of this study is to investigate the strengths and weaknesses of each approach from trust, revocation, privacy, and security perspectives. We examine the technical features and privacy and security mechanisms of each technology and provide a comparative synthesis with the proposed model. Through our analysis, we demonstrate that each technology offers unique advantages and limitations, and the integration of these technologies can lead to more secure and efficient solutions for authentication and access to scientific data. © 2023 ACM.

关键词： Smart contract

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From User Stories to Domain Models: Recommending Relationships between Entities

From User Stories to Domain Models: Recommending Relationshi...

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Joint of REFSQ-2023 Workshops, Doctoral Symposium, Posters and Tools Track and Journal Early Feedback, REFSQ-JP 2023

作者： Bragilovski, Maxim Dalpiaz, Fabiano Sturm, Arnon Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Israel Department of Information and Computing Sciences Utrecht University Netherlands

User stories are a common notation for expressing requirements, especially in agile development projects. While user stories provide a detailed account of the functional requirements, they fail to deliver a holistic view of the domain. As such, they can be complemented with domain models that not only help gain this comprehensive view, but also serve as a basis for model-driven development. We focus on the task of recommending relationships between entities in a domain model, assuming that these entities were previously extracted from a user story collection either manually or through an automated tool. We investigate whether an approach based on supervised machine learning can recommend essential relationships in a domain model more accurately than state-of-the-art rule-based methods. Based on a collection of datasets that we manually labeled and a set of 32 features we engineered, we train a machine learning model by using a random forest classifier. The results indicate that our approach has higher precision and F1-score than the baseline rule-based methods. Our findings provide preliminary evidence of the suitability of using machine learning to support the development of domain models, especially in recommending relationships between related entities. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Supervised learning

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MLPFormer: MLP-integrated transformer for colorectal histopathology whole slide image segmentation

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Neural computing and Applications 2025年第6期37卷 4651-4661页

作者： Wang, Yuxuan Li, Dan Li, Xuechen Guo, Yan Zuo, Yanfei Shen, Linlin Department of Computer Science Wenzhou-Kean University Wenzhou China School of Electronics and Information Engineering Wuyi University Jiangmen China Department of Histo Pathology Shanghai Histo Pathology Diagnostic Center Shanghai China College of Computer Sicence and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Colorectal intraepithelial neoplasia is a precancerous lesion of colorectal cancer, which is mainly diagnosed using pathological images. According to the characteristics of lesions, precancerous lesions can be classified into four different grades, i.e., background, normal, low and high level. However, due to the continuity of lesion development, tissue in different stages has high similarity. Recently, visual transformers have achieved impressive results in different visual tasks, due to their capacity of capturing long-range dependencies. However, due to computational cost, transformer cannot well extract detailed feature, which is important for lesion grading. To accurately segment pathological tissues with high similarity, in this work, we embed the multi-head MLP mixer module in the transformer network to extract cell and edge information. Furthermore, we propose a hierarchical MLP decoder to facilitate multi-scale feature fusion. The proposed transformer, namely MLPFormer, achieves remarkable results against competitive baselines on the Histo-CRC Biopsy dataset. The experimental results demonstrate that MLPFormer significantly outperforms the competitive baselines, i.e., a 3% dice improvement is achieved over the SegFormer series. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image segmentation

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Inclusive Gamification: An Exploratory Study in software Development Enterprises 35

Inclusive Gamification: An Exploratory Study in Software Dev...

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35th International Conference on software engineering and Knowledge engineering, SEKE 2023

作者： Elsalmy, Fayrouz Sherief, Nada Hany Abdelmoez, Walid Information Systems College of Computing and Information Technology Arab Academy for Science and Technology Alexandria Egypt Software engineering College of Computing and Information Technology Arab Academy for Science and Technology Alexandria Egypt

Gamification is the incorporation of game elements into non-game settings. software designers increase user motivation by introducing adequately engaging elements, such as leaderboards and badges, into an existing system. In recent years, few studies have examined the risks associated with gamification inside the business systems sector, especially within software development organizations. Yet, this issue must be addressed more systematically to build gamified solutions that take individual gamification risks into account. This research introduces "Inclusive Gamification" as contextually aware gamification that highlights individual risks. The purpose of this paper is twofold: 1) to identify the gamification risk factors that could cause the emergence of risks in software development organizations;and 2) to develop a list of risks with consideration of various contextual factors, namely tasks, personality type, adopted game element, and organizational cultural aspects. 15 employees from three software development companies participated in an exploratory study in which their perspectives on the negative effects of gamification were collected. As a result, we were able to identify several risk factors and associated risks. Our findings assist software engineers throughout the design process in identifying potential threats. Thus, create gamified solutions that better situate employees at their workplaces. © 2023 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Personnel

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IBATree: A Novel Method for Interpretable Cancer Cell Diagnosis Using Information Bottleneck Attribution

IBATree: A Novel Method for Interpretable Cancer Cell Diagno...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Raza, Muhammad Umair Chen, Jie Nawaz, Adil Saeed, Faisal Leung, Victor C.M. Li, Jianqiang Wang, Zhaoxia Shenzhen University College of Computer Science and Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China Shenzhen Children's Hospital Department of Gastroenterology China

ISBN: (纸本)9798350386226

Deep Neural Networks (DNNs) have demonstrated remarkable performance in classification and regression tasks on RGB-based pathological inputs. The network's prediction mechanism must be interpretable to establish trust in the clinical routine. One principal approach to interpretation is feature attribution. Feature attribution methods identify the importance of input features for the output prediction. Building on the Information Bottleneck Attribution (IBA) method, we recognize the RGB's input regions with high mutual information with the network's output for each prediction. IBA identifies input regions that have sufficient predictive information. In this paper, we introduce "IBATree", a novel approach that combines IBA with decision trees to enhance both the interpretability and accuracy of DNNs in cancer cell classification. Our method leverages the information bottleneck framework to inject noise into feature maps and then isolates the most informative features for model predictions while maintaining high classification performance. We evaluated our proposed approach on three datasets - CNMC, ISBI2016, and BreaKHis - demonstrating competitive accuracy and producing clear interpretations. This makes IBATree particularly suitable for clinical applications, where understanding the rationale behind predictions is crucial. Our results show that IBATree provides reliable predictions and valuable insights into feature importance, paving the way for its application in various biomedical domains. © 2024 IEEE.

关键词： Diseases

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