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36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023

作者： Avgeris, Marios Leivadeas, Aris Athanasopoulos, Nikolaos Lambadaris, Ioannis Falkner, Matthias Carleton University Department of Systems and Computer Engineering Ottawa Canada Ácole de Technologie Supérieure Department of Software and It Engineering Montreal Canada Queen's University School of Electronics Electrical Engineering and Computer Science Belfast United Kingdom Cisco Systems Ottawa Canada

ISBN: (纸本)9781665477161

Recent trends in Network Function Virtualization (NFV) combined with Internet of Things (IoT) and 5G applications have reshaped the network service offering. In particular, Service Function Chains (SFCs) can associate network functions with physical and virtual resources towards providing a complete network service. Concurrently, the management of a continuously expanding network and the fulfillment of the applications' requirements pave the way for autonomic network solutions. Intent Based Networking (IBN) is a novel paradigm that aims to achieve the automatic orchestration of network services and the assurance of their performance. Accordingly, in this paper, we propose a novel automated network assurance model, based on Model Predictive Control, to guarantee the Quality of Service (QoS) and security requirements of multi-tenant and IBN-enabled SFCs. In this context, corrective decisions are proactively taken, in the form of incoming intent relocations among the SFCs. The results reveal that our model can assure with high probability the application requirements and minimize QoS violations. © 2023 IEEE.

关键词： Network function virtualization

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Breast Microcalcification detection in digital mammograms using Deep Transfer learning approaches 23

Breast Microcalcification detection in digital mammograms us...

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9th International Conference on Computing and Data engineering, ICCDE 2023

作者： Rasool, Ehtsham Anwar, Muhammad Junaid Shaker, Bilawal Hashmi, Muhammad Harris Rehman, Khalil Ur Seed, Yousaf Department of Computer Science The University of Alabama Birmingham United States School of Software Engineering Beijing University of Technology Beijing China

ISBN: (纸本)9781450398022

Breast cancer is the most often diagnosed cancer in women affecting one in eight at the age of 80 in US. Breast is the most threatening cancer among women which leads to death. Early diagnosis of breast cancer can save their lives which decreases the mortality rate. Mammography is a standard screening method for breast cancer diagnosis that identifies occurrences of breast cancer in women's at early stages without symptoms. In this study, we employed transfer learning in deep learning to increase the neural network's performance and reduce the false positive rate. In addition, we proposed a pre-trained VGG-19 neural network to extract features of individual microcalcification to predict breast cancer. The proposed method was evaluated on two public databases the CBIS-DDSM and DDSM and achieved 0.98 sensitivities respectively. The proposed method obtained higher sensitivity than other residual neural networks and previous studies. © 2023 ACM.

关键词： Diseases

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DCC: Differentiable Cardinality Constraints for Partial Index Tracking 39

DCC: Differentiable Cardinality Constraints for Partial Inde...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Jo, Wooyeon Cho, Hyunsouk Department of Artificial Intelligence Ajou University Suwon Korea Republic of Department of Software and Computer Engineering Ajou University Suwon Korea Republic of The AI Lab Inc. Seoul Korea Republic of

ISBN: (纸本)157735897X

Index tracking is a popular passive investment strategy aimed at optimizing portfolios, but fully replicating an index can lead to high transaction costs. To address this, partial replication have been proposed. However, the cardinality constraint renders the problem non-convex, non-differentiable, and often NP-hard, leading to the use of heuristic or neural network-based methods, which can be non-interpretable or have NP-hard complexity. To overcome these limitations, We propose a Differentiable Cardinality Constraint (DCC) for index tracking and introduce a floating-point precision-aware method to address implementation issues. We theoretically prove our methods calculate cardinality accurately and enforce actual cardinality with polynomial time complexity. We propose the range of the hyperparameter ensures that our method has no error in real implementations, based on theoretical proof and experiment. Our method applied to mathematical method outperforms baseline methods across various datasets, demonstrating the effectiveness of the identified hyperparameter. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Heuristic methods

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Centralized Machine Learning Versus Federated Averaging: A Comparison using MNIST Dataset

Centralized Machine Learning Versus Federated Averaging: A C...

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作者： Peng, Sony Yang, Yixuan Mao, Makara Park, Doo-Soon Department of Software Convergence Soonchunhyang University Korea Republic of Department of Computer Software Engineering Soonchunhyang University Korea Republic of

A flood of information has occurred with the rise of the internet and digital devices in the fourth industrial revolution era. Every millisecond, massive amounts of structured and unstructured data are generated;smartphones, wearable devices, sensors, and self-driving cars are just a few examples of devices that currently generate massive amounts of data in our daily. Machine learning has been considered an approach to support and recognize patterns in data in many areas to provide a convenient way to other sectors, including the healthcare sector, government sector, banks, military sector, and more. However, the conventional machine learning model requires the data owner to upload their information to train the model in one central location to perform the model training. This classical model has caused data owners to worry about the risks of transferring private information because traditional machine learning is required to push their data to the cloud to process the model training. Furthermore, the training of machine learning and deep learning models requires massive computing resources. Thus, many researchers have jumped to a new model known as "Federated Learning". Federated learning is emerging to train Artificial Intelligence models over distributed clients, and it provides secure privacy information to the data owner. Hence, this paper implements Federated Averaging with a Deep Neural Network to classify the handwriting image and protect the sensitive data. Moreover, we compare the centralized machine learning model with federated averaging. The result shows the centralized machine learning model outperforms federated learning in terms of accuracy, but this classical model produces another risk, like privacy concern, due to the data being stored in the data center. The MNIST dataset was used in this experiment. Copyright © 2022 KSII.

关键词： Deep neural networks

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How (Un)Faithful is Attention? 5

How (Un)Faithful is Attention?

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5th Workshop on Analyzing and Interpreting Neural Networks for NLP, BlackboxNLP 2022 hosted by the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Amini, Hessam Kosseim, Leila Laboratory Department of Computer Science and Software Engineering Concordia University Montreal Canada

ISBN: (纸本)9781959429050

Although attention weights have been commonly used as a means to provide explanations for deep learning models, the approach has been widely criticized due to its lack of faithfulness. In this work, we present a simple approach to compute the newly proposed metric AtteFa, which can quantitatively represent the degree of faithfulness of the attention weights. Using this metric, we further validate the effect of the frequency of informative input elements and the use of contextual vs. non-contextual encoders on the faithfulness of the attention mechanism. Finally, we apply the approach on several real-life binary classification datasets to measure the faithfulness of attention weights in real-life settings. © 2022 Association for Computational Linguistics.

关键词： Classification (of information)

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Energy Efficiency Issues in Android Application: A Literature Review 24

Energy Efficiency Issues in Android Application: A Literatur...

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24th International Multitopic Conference, INMIC 2022

作者： Ullah, Obaid Hanan, Muhammad Ghafoor, Maryam Abdul National University of Computer and Emerging Sciences Department of Software Engineering Islamabad Pakistan

ISBN: (数字)9798350397109

ISBN: (纸本)9798350397109

In today's digital world, almost every person owns a smartphone device. Due to more emphasis on the functional aspect of an application, programmers often follow such practices that consume a lot of energy. Hence, the purpose of this literature review is to find such issues that can cause more energy consumption in the android applications along with finding their solutions from the literature. The literature review also includes year-wise and venue-wise paper distribution. Out of our initial 145 papers, we discarded 4 papers based on a duplicate study, then 100 papers were discarded on the title and abstract-based screening while 22 papers were discarded based on inclusion/exclusion and quality assurance criteria. A final of 19 studies were considered for this study and were read thoroughly. Our results reveal that bad programming practice was the most discussed issue (26%) while tool-related problems and patterns were the least discussed issues in the literature (15.7%). Tool-based solutions are discussed mostly (36.84%) while refactoring technique and applying other techniques are discussed least (10.5%) in the literature. The work is helpful for the researchers and developers as they can learn from this about the energy consumption reasons and their solutions. © 2022 IEEE.

关键词： Electric power utilization

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EPM: Evolutionary Perception Method for Anomaly Detection in Noisy Dynamic Graphs

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IEEE Transactions on Knowledge and Data engineering 2025年第7期37卷 4035-4048页

作者： Wang, Huan Chen, Junyang Wu, Yirui Leung, Victor C. M. Wang, Di Huazhong Agricultural University Key Laboratory of Smart Farming for Agricultural Animals Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education College of Informatics Wuhan430072 China King Abdullah University of Science and Technology Division of Computer Electrical and Mathematical Sciences and Engineering Thuwal23955 Saudi Arabia Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Hohai University College of Computer and Information Nanjing210098 China Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518115 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

With the rapid expansion of interactions across various domains such as social networks, transaction networks, and IP-IP networks, anomaly detection in dynamic graphs has become increasingly critical for mitigating potential risks. However, existing anomaly detection methods often assume noise-free dynamic graphs, overlooking the prevalence of noisy dynamic graphs in real-world applications. Specifically, noisy dynamic graphs affected by structural noises—such as spurious and missing nodes and edges—struggle to consistently provide reliable structural evidence for anomaly detection. To tackle this challenge, we propose an Evolutionary Perception Method (EPM) for identifying anomalous nodes in noisy dynamic graphs by resisting the interference of structural noises. EPM primarily consists of two components: a dynamic fitter and a filtering reviser. The dynamic fitter characterizes the interaction dynamics of nodes that removes and generates links at each period as a multiple superposition state, utilizing various link prediction algorithms to fit evolutionary mechanisms. Additionally, the filtering reviser designs evolutional entropies to quantify the evolutional uncertainty in multiple superposition states, further reconstructing the Kalman filter to optimize these entropies. Extensive experiments have proved that our proposed EPM outperforms state-of-the-art methods in discovering anomalous nodes in noisy dynamic graphs. © 1989-2012 IEEE.

关键词： Kalman filters

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Towards Secure Agile software Development Process: A Practice-Based Model 48

Towards Secure Agile Software Development Process: A Practic...

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48th Euromicro Conference on software engineering and Advanced Applications, SEAA 2022

作者： Ardo, Abdulhamid A. Bass, Julian M. Gaber, Tarek University of Salford Department of Computer Science & Software Engineering Manchester United Kingdom

ISBN: (纸本)9781665461528

Agile methods are a well-established paradigm in the software development field. Agile adoption has contributed to improving software quality. However, software products are vulnerable to security challenges and susceptible to cyberattacks. This study aims to improve security of software products when using an agile software development process. A multi-methods qualitative research approach was adopted in this study. First, we conducted semi-structured interviews with 23 agile practitioners having varied years of cybersecurity experiences. An approach informed by grounded theory methodology was adopted for data analysis. Second, we developed a novel practice-based agile software development process model derived from the results of the data analysis conducted. Third, we validated the model through a focus group comprising five senior agile cybersecurity professionals to evaluate its relevancy and novelty. The study has identified 26 security practices, organized into the six - software development life-cycle phases: planning, requirements, design, implementation, testing, and deployment. We have mapped the practices onto four swim lanes each representing an agile role. The self-organizing team is exclusively involved in three security practices, the security specialist in nine, penetration tester in one and the DevOps team collaborates on one with the security specialist. There are also seven practices that are collaboratively performed by the self-organizing team and the security specialist. Each of the practices in the model was examined during the validation phase of the study. There are two contributions in this study. First, the paper proposes a novel practice-based model comprising of 26 security practices mapped to agile roles. Second, we propose a new practice, in response to an observed lack of collaborative ceremonies, to disseminate awareness of and hence compliance with security standards. © 2022 IEEE.

关键词： software testing

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Bibliometric analysis of artificial intelligence and optical coherence tomography images:research hotspots and frontiers

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International Journal of Ophthalmology(English edition) 2023年第9期16卷 1431-1440页

作者： Hai-Wen Feng Jun-Jie Chen Zhi-Chang Zhang Shao-Chong Zhang Wei-Hua Yang Department of Software Engineering School of SoftwareShenyang University of TechnologyShenyang 110870Liaoning ProvinceChina Department of Computer School of Intelligent MedicineChina Medical UniversityShenyang 110122Liaoning ProvinceChina Shenzhen Eye Hospital Jinan UniversityShenzhen 518040Guangdong ProvinceChina

AIM:To explore the latest application of artificial intelligence(AI)in optical coherence tomography(OCT)images,and to analyze the current research status of AI in OCT,and discuss the future research ***:On June 1,2023,a bibliometric analysis of the Web of Science Core Collection was performed in order to explore the utilization of AI in OCT *** parameters such as papers,countries/regions,citations,databases,organizations,keywords,journal names,and research hotspots were extracted and then visualized employing the VOSviewer and CiteSpace V bibliometric ***:Fifty-five nations reported studies on AI biotechnology and its application in analyzing OCT *** United States was the country with the largest number of published ***,197 institutions worldwide provided published articles,where University of London had more publications than the *** reference clusters from the study could be divided into four categories:thickness and eyes,diabetic retinopathy(DR),images and segmentation,and OCT ***:The latest hot topics and future directions in this field are identified,and the dynamic evolution of AIbased OCT imaging are ***-based OCT imaging holds great potential for revolutionizing clinical care.

关键词： artificial intelligence optical coherence tomography bibliometric deep learning machine learning

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software Product System Model: A Customer-Value Oriented, Adaptable, DevOps-Based Product Model

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SN computer Science 2022年第1期3卷 38页

作者： Altunel, Haluk Say, Bilge Softtech Inc. Ankara Turkey Department of Computer Engineering Bilkent University Ankara Turkey Department of Software Engineering Atilim University Ankara Turkey

DevOps pipelines have brought notable advantages, such as fast and frequent software delivery to software production paradigms, but dynamically dealing with quality attributes desired by the customer employing a DevOps pipeline remains a challenge. This work aims to define the design of a systems thinking inspired model, called software Product System Model (SPSM), applying a customer-value oriented, holistic approach for implementing quality requirements, and its application and evaluation in a large software house. The main features include dynamic control of quality gates, the parameters of which are driven by customer requirements and feedback from surveys. All of the inputs are collected in a product backlog and fed forward to the quality gates over the DevOps pipeline. SPSM was successfully deployed in a large software house extending a DevOps pipeline with an accompanying improvement of customer-value oriented key performance indicators for projects. In a 2-year-long case study, security and code quality were the main quality attributes, with the metrics on security vulnerabilities and unit test coverage. At the end of the 2020, the DevOps pipeline within SPSM provided a 69.50% decrease of security vulnerabilities of all software products, and a 29.43% increase in unit test coverage for the whole code base for increasing code quality. At the end of 2020, the project completion ratio was measured to be 99.50% and the Schedule Performance Index (SPI) was measured to be 99.78% as the average of 762 projects delivered. The flexibility of SPSM allowed the software house to adapt to changing customer expectations. A checklist is provided for the replicability of the model application. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2021.

关键词： Customer value DevOps software attributes software metrics software product management software Product System Model Systems thinking

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