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11th IEEE Open Conference of Electrical, Electronic and Information sciences, eStream 2024

作者： Lagnaoui, Saloua Boumais, Khaoula Fallah, Saad El En-Naimani, Zakariae Haddouch, Khalid Matuzevicius, Dalius ENSA Sidi Mohamed Ben Abdellah University Laboratory Engineering Systems and Applications Fez Morocco Sidi Mohammed Ben Abdallah University ENSA Laboratory AI Data Science and Emerging Systems Fez Morocco Private University of Fez Systems and Sustainable Environment Laboratory Faculty of Engineering Sciences Fez Morocco ENSET Laboratory Computer Science AI and Cyber Security University of Hassan II Casablanca Mohammedia Morocco Vilnius Gediminas Technical University Faculty of Electronics Department of Electronic Systems Vilnius Lithuania

ISBN: (纸本)9798350352412

Convolutional Neural Networks are instrumental in artificial intelligence, especially in image processing, where their ability to autonomously learn hierarchical features has led to significant breakthroughs. However, the success of these models is intricately tied to the judicious choice of hyperparameters, which include the configuration of convolutional layers, activation functions, and kernel initialization methods. This research explores kernel initialization methods in Convolutional Neural Network models, seven diverse initialization methods (Glorot Uniform, Ones initialization, Zero initialization, Constant initialization, Random initialization, HeNormal initialization, and Orthogonal initialization) are comprehensively compared. The primary objective is to showcase the sensitivity of Convolutional Neural Networks to these various initialization techniques. The study not only aims to reveal the nuanced impact of kernel initialization but also introduces an adaptive method to enhance model performance. By delving into the intricacies of initialization methods, this research contributes to the improvement of Convolutional Neural Networks effectiveness, especially in critical applications like Plant Disease classification. © 2024 IEEE.

关键词： Convolutional neural networks

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Iterative Intraoperative Digital Tomosynthesis Image Reconstruction using a Prior as Initial Image 7

Iterative Intraoperative Digital Tomosynthesis Image Reconst...

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7th International Conference on Image Formation in X-Ray Computed Tomography

作者： Saad, Fatima Frysch, Robert Pfeiffer, Tim Saalfeld, Sylvia Schulz, Jessica Georgi, Jens-Christoph Nürnberger, Andreas Lauritsch, Guenter Rose, Georg Institute for Medical Engineering Otto-von-Guericke University Magdeburg Germany Forschungscampus STIMULATE Otto-von-Guericke University Magdeburg Germany Faculty of Computer Science Otto-von-Guericke University Magdeburg Germany Siemens Healthcare GmbH Forchheim Germany Data and Knowledge Engineering Group Faculty of Computer Science Otto-von-Guericke University Magdeburg Germany

ISBN: (纸本)9781510656697

The efficacy of interventional treatments highly relies on an accurate identification of the target lesions and the interventional tools in the guidance images. Whereas X-ray radiography poses low doses to the patient, its weakness is in the superposition of the different image structures in a 2D image. Cone-beam computed tomography (CBCT) might look ideal providing exact 3D information, however this is at the cost of a higher radiation dose, longer imaging time, and more space requirements in the operating room. Introducing some depth information with relatively low dose, and requiring less space, digital tomosynthesis (DTS) is a potential candidate for guiding interventions. However, due to the few number of projections and to the limited angle acquisition, DTS has poor depth resolution. Since high quality patient-specific prior CT scans are usually performed prior to the intervention for diagnosis or to plan the intervention, and given that such images share a fair amount of information with the intraoperative DTS images, we propose in this work a prior-based iterative reconstruction framework to improve the intraoperative DTS image quality. The framework is based on registering the prior CT image to an intermediate low-quality intraoperative DTS image, then iteratively re-reconstructing the intraoperative DTS image using the coregistered prior CT as the starting image. We acquired prior CT and intraoperative CBCT data of a liver phantom and simulated some intraoperative DTS projection images using a spherical ellipse scan geometry. Our results show a great improvement in the DTS image quality with the proposed method and prove the importance of choosing a good starting point for the iterative DTS reconstruction. © 2022 SPIE.

关键词： computerized tomography

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Fast Semi-supervised Unmixing using Non-convex Optimization

arXiv

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arXiv 2024年

作者： Rasti, Behnood Zouaoui, Alexandre Mairal, Julien Chanussot, Jocelyn Faculty of Electrical Engineering and Computer Science Technische Universität Berlin the Berlin Institute for the Foundations of Learning and Data Berlin Germany Univ. Grenoble Alpes Inria CNRS Grenoble INP LJK Grenoble38000 France Data Science Experts Grenoble France

In this paper, we introduce a novel linear model tailored for semisupervised/library-based unmixing. Our model incorporates considerations for library mismatch while enabling the enforcement of the abundance sum-to-one constraint (ASC). Unlike conventional sparse unmixing methods, this model involves nonconvex optimization, presenting significant computational challenges. We demonstrate the efficacy of Alternating Methods of Multipliers (ADMM) in cyclically solving these intricate problems. We propose two semisupervised unmixing approaches, each relying on distinct priors applied to the new model in addition to the ASC: sparsity prior and convexity constraint. Our experimental results validate that enforcing the convexity constraint outperforms the sparsity prior for the endmember library. These results are corroborated across three simulated datasets (accounting for spectral variability and varying pixel purity levels) and the Cuprite dataset. Additionally, our comparison with conventional sparse unmixing methods showcases considerable advantages of our proposed model, which entails nonconvex optimization. Notably, our implementations of the proposed algorithms—fast semisupervised unmixing (FaSUn) and sparse unmixing using soft-shrinkage (SUnS)—prove considerably more efficient than traditional sparse unmixing methods. SUnS and FaSUn were implemented using PyTorch and provided in a dedicated Python package called Fast Semisupervised Unmixing (FUnmix), which is open-source and available at https://***/BehnoodRasti/FUnmix. Copyright © 2024, The Authors. All rights reserved.

关键词： Convex optimization

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Edge-Cloud VPN Traffic Analysis over Cross Platforms

Edge-Cloud VPN Traffic Analysis over Cross Platforms

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Haotian Liu Riyad Alshammari Nur Zincir-Heywood Faculty of Computer Science Dalhousie University Halifax NS Canada National Center for AI (NCAI) Saudi Authority for Data and Artificial Intelligence (SDAIA) Riyadh KSA

ISBN: (数字)9798350373011

ISBN: (纸本)9798350373028

The escalating demand for user privacy and data encryption has significantly boosted the volume of encrypted traffic on the contemporary Internet. Nonetheless, there is a shortage of comprehensive research on the properties of Virtual Private Network traffic. In this paper, we explore Edge-Cloud Virtual Private Network traffic in order to understand usage patterns without deep packet inspection. To achieve this, we use a machine learning based approach to analyze traffic flows, and identify Virtual Private Network traffic captured from five applications that belong to four service categories. Our results demonstrate that we are not only able to identify the VPN traffic, but also able to identify the different applications and therefore service categories across different network locations and computing platforms.

关键词： data privacy Machine learning Inspection Virtual private networks Encryption Internet of Things

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A Novel Particle Swarm Optimization Algorithm for Meta-Heuristic Analysis Mechanism Based on Population Learning Strategies and Adaptive Selection of Leadership Particles

A Novel Particle Swarm Optimization Algorithm for Meta-Heuri...

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International Conference on data science and Advanced Analytics (DSAA)

作者： Yuheng Wu Xingyu Zhu Wenxu Zhao Xiaona Xia School of Computer Science Qufu Normal University Rizhao China Faculty of Education Chinese Academy of Education Big Data Qufu Normal University Qufu China

ISBN: (数字)9798350364941

ISBN: (纸本)9798350364958

To improve the particle swarm optimization algorithm's population diversity and global search ability, this study proposes a novel particle swarm optimization algorithm for meta-heuristic analysis mechanism based on population learning strategies and adaptive selection of leadership particles (ASLPSO) to achieve the fusion of population learning strategy and adaptive selection method of leadership particles. The particle swarm is adaptively separated into several populations through density peak clustering. Meanwhile, a new learning strategy is designed for analyzing the local optimal particles of each subgroup, that might enable the ordinary particles to learn effectively. The global optimum is relatively ensured by comparing the fitness value of each local optimal particle, which might obtain the best performances of multi-peak reference functions. After comparing with the approximate algorithms on multiple benchmark functions, it is found that the standard deviation and error mean value are improved. Therefore, ASLPSO has enhanced the population diversity and global search ability, prevented the algorithm from falling into premature too early.

关键词： Leadership Metaheuristics Clustering algorithms data science Benchmark testing Approximation algorithms Robustness Particle swarm optimization Time complexity Standards

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WIT-Greedy: Hardware System Design of Weighted ITerative Greedy Decoder for Surface Code

WIT-Greedy: Hardware System Design of Weighted ITerative Gre...

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Asia and South Pacific Design Automation Conference

作者： Wang Liao Yasunari Suzuki Teruo Tanimoto Yosuke Ueno Yuuki Tokunaga Photon Science Center the University of Tokyo Japan NTT Computer and Data Science Laboratories Japan Faculty of Information Science and Electrical Engineering Kyushu University Japan Graduate School of Information Science and Technology the University of Tokyo Japan

Large error rates of quantum bits (qubits) are one of the main difficulties in the development of quantum computing. Performing quantum error correction (QEC) with surface codes is considered the most promising approach to reduce the error rates of qubits effectively. To perform error correction, we need an error-decoding unit, which estimates errors in the noisy physical qubits repetitively, to create a robust logical qubit. While complicated graph-matching problems must be solved within a strict time restriction for the error decoding, several hardware implementations that satisfy the restriction at a large code distance have been proposed. However, the existing decoder designs are still challenging in reducing the logical error rate. This is because they assume that the error rates of physical qubits are uniform while they have large variations in practice. According to our numerical simulation based on the quantum chip with the largest qubit number, neglecting the non-uniform error properties of a real quantum chip in the decoding process induces significant degradation of the logical error rate and spoils the benefit of QEC. To take the non-uniformity into account, decoders need to solve matching problems on a weighted graph, but they are difficult to solve using the existing designs without exceeding the time limit of decoding. Therefore, a decoder that can treat both the non-uniform physical error rates and the large surface code is strongly demanded. In this paper, we propose a hardware design of decoding units for the surface code that can treat the non-identical error properties with small latency at a large code distance. The key idea of our design is 1) constructing a look-up table for calculating the shortest paths between nodes in a weighted graph and 2) enabling parallel processing during decoding. The implementation results in field programmable gate array (FPGA) indicate that our design scales up to code distance 11 within a microsecond-level delay,

关键词： Codes Error analysis Qubit Hardware Decoding Delays Table lookup

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Leveraging Deep Learning for Improved Sentiment Analysis in Natural Language Processing 3

Leveraging Deep Learning for Improved Sentiment Analysis in ...

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3rd Odisha International Conference on Electrical Power Engineering, Communication and Computing Technology, ODICON 2024

作者： Kulkarni, Aniket Gollavilli, Venkata Surya Bhavana Harish Alsalami, Zaid Bhatia, Manpreet Kaur Jovanovska, Sashka Absur, Md Nurul Drata Inc San DiegoCA United States Under Armour MD United States College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Asian School of Media Studies Department of Data Science Noida India Goce Delcev University Faculty of Philology Department of English Language and Literature Stip Macedonia Cuny Graduate Center Computer Science New York United States

ISBN: (纸本)9798350354379

Sentiment analysis is viewed as quite possibly of the main works in mental science and normal language handling. To work on the productivity of sentiment analysis techniques, it is crucial for separate the useful words that add to the grouping choice as well as characterize the sentences as indicated by their profound names. Profound brain networks that depend on the consideration component have taken huge steps toward this path as of late. Reads up on consideration processes for message arrangement, and especially sentiment analysis, are as yet not many, in any case. This research fills this gap by presenting a Convolution Neural Network (CNN) combined with an attention layer that can extract relevant words and give them greater weights according to the context. The suggested model uses a context vector at the attention layer and attempts to gauge a word's relevance based on how similar the word vector and context vector are to one another. New vectors from the consideration layer are incorporated into sentence vectors and utilised for organization once they have been supplied. The suggested model was validated using a small number of tests on the Stanford datasets. The trial discoveries show that the recommended model works far superior to past research studies and can separate significant expressions from setting that have an incentive for analysis and application. © 2024 IEEE.

关键词： Convolutional neural networks

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Segment Together: A Versatile Paradigm for Semi-Supervised Medical Image Segmentation

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Zeng, Qingjie Xie, Yutong Lu, Zilin Lu, Mengkang Wu, Yicheng Xia, Yong Northwestern Polytechnical University National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Xi’an710072 China The University of Adelaide Australian Institute for Machine Learning AdelaideSA5000 Australia Monash University Faculty of Information Technology Department of Data Science and AI Australia

The scarcity of annotations has become a significant obstacle in training powerful deep-learning models for medical image segmentation, limiting their clinical application. To overcome this, semi-supervised learning that leverages abundant unlabeled data is highly desirable to enhance model training. However, most existing works still focus on specific medical tasks and underestimate the potential of learning across diverse tasks and datasets. In this paper, we propose a Versatile Semi-supervised framework (VerSemi) to present a new perspective that integrates various SSL tasks into a unified model with an extensive label space, exploiting more unlabeled data for semi-supervised medical image segmentation. Specifically, we introduce a dynamic task-prompted design to segment various targets from different datasets. Next, this unified model is used to identify the foreground regions from all labeled data, capturing cross-dataset semantics. Particularly, we create a synthetic task with a CutMix strategy to augment foreground targets within the expanded label space. To effectively utilize unlabeled data, we introduce a consistency constraint that aligns aggregated predictions from various tasks with those from the synthetic task, further guiding the model to accurately segment foreground regions during training. We evaluated our VerSemi framework against seven established SSL methods on four public benchmarking datasets. Our results suggest that VerSemi consistently outperforms all competing methods, beating the second-best method with a 2.69% average Dice gain on four datasets and setting a new state of the art for semi-supervised medical image segmentation. Code is available at https://***/maxwell0027/VerSemi. © 1982-2012 IEEE.

关键词： Self-supervised learning

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Automatic mapping of gully from satellite images using asymmetric non-local LinkNet: A case study in Northeast China

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International Soil and Water Conservation Research 2024年第2期12卷 365-378页

作者： Panpan Zhu Hao Xu Ligang Zhou Peixin Yu Liqiang Zhang Suhong Liu College of Computer Sciences and Technology Chongqing University of Posts and TelecommunicationsChongqing Engineering Research Center for Spatial Big Data Intelligent TechnologyChongqing400065China College of Environment and Ecology Chongqing UniversityChongqing400044China Chongqing Municipal Ecological and Environmental Monitoring Center Chongqing401120China Beijing Key Laboratory of Environmental Remote Sensing and Digital Cities Faculty of Geographical ScienceBeijing Normal UniversityBeijing100875China China National Environmental Monitoring Centre Beijing100012China

Gully erosion can lead to the destruction of farmland and the reduction in crop *** mapping from remote sensing images is critical for quickly obtaining the distribution of gullies at regional scales and arranging corresponding prevention and control *** narrow and irregular shapes and similar colors to the surrounding farmland make mapping erosion gullies in sloping farmland from remote sensing images *** implement gully erosion mapping,we developed a small training samples-oriented lightweight deep leaning model,called asymmetric non-local LinkNet(ASNL-LinkNet).The ASNL-LinkNet integrates global context information through an asymmetric non-local operation and conducts multilayer feature fusion to improve the robustness of the extracted *** results show that the proposed ASNL-LinkNet achieves the best performance when compared with other deep learning *** quantitative evaluation results in the three test areas show that the F1-score of erosion gully recognition varies from 0.62 to *** study provides theoretical reference and practical guidance for monitoring erosion gullies on slope farmland in the black soil region of Northeast China.

关键词： Gully erosion mapping High-resolution satellite images Deep learning Black soil

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A framework for improving the reliability of black-box variational inference

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 10599-10669页

作者： Manushi Welandawe Michael Riis Andersen Aki Vehtari Jonathan H. Huggins Department of Mathematics & Statistics Boston University DTU Compute Technical University of Denmark Denmark Department of Computer Science Aalto University Finland Department of Mathematics & Statistics Faculty of Computing & Data Sciences Boston University

Black-box variational inference (BBVI) now sees widespread use in machine learning and statistics as a fast yet exible alternative to Markov chain Monte Carlo methods for approximate Bayesian inference. However, stochastic optimization methods for BBVI remain unreliable and require substantial expertise and hand-tuning to apply effectively. In this paper, we propose robust and automated black-box VI (RABVI), a framework for improving the reliability of BBVI optimization. RABVI is based on rigorously justiffed automation techniques, includes just a small number of intuitive tuning parameters, and detects inaccurate estimates of the optimal variational approximation. RABVI adaptively decreases the learning rate by detecting convergence of the fixed-learning-rate iterates, then estimates the symmetrized Kullback-Leibler (KL) divergence between the current variational approximation and the optimal one. It also employs a novel optimization termination criterion that enables the user to balance desired accuracy against computational cost by comparing (i) the predicted relative decrease in the symmetrized KL divergence if a smaller learning were used and (ii) the predicted computation required to converge with the smaller learning rate. We validate the robustness and accuracy of RABVI through carefully designed simulation studies and on a diverse set of real-world model and data examples.

关键词： black-box variational inference symmetrized KL divergence stochastic optimization fixed-learning rate

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