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2nd International Conference on Smart computing and Electronic Enterprise, ICSCEE 2021

作者： Baker El-Ebiary, Yousef A. Iryani Ahmad Saany, Syarilla Aledinat, Lowai Saleh Ravindran Pathmanathan, P. Jusoh, Julaily Aida Aseh, Khairi Mohamed, Rajina R. Nazari Mohd Rose, Ahmad Moaiad, Yazeed Al UniSZA Faculty of Informatics and Computing Malaysia International Institute of Applied Science Swiss School of Management Netherlands Universiti Tenaga Nasional College of Computing Dan Informatics Malaysia UniSZA University Faculty of Informatics and Computing Malaysia MEDIU Faculty of Computer and Information Technology Malaysia

ISBN: (纸本)9781665432221

A home helper is in high demand these days because it relieves the stress of doing household chores. Home helpers are usually self-employed and work part-time in their clients' homes. Be that as it may, having domestic helper departments within the nearby local area is annoying since residents, in general, have no foggy idea of when or how to contact the person providing these types of help. Examples of departments that could be offered include escort departments, garden maintenance, dinner planning, pet grooming, assistance with family tasks, or even friendship for the elderly. It is clear that there is a gap between the specialized household cooperatives and the neighboring local area that requested the departments. Usually, individuals introducing departments present their administrations by using flyers or bypassing their phone numbers to individuals. This is definitely not an accurate display system because the dispersion of the data is limited to just a small aggregation of the surrounding local area. Going forward, I-HomeHelper acts as a unique stage for people to bring their department to an end through openness who are asking their departments to take advantage of current developments in Global Positioning Systems (GPS) with Industry Revolution 4.0 (IR4.0). I-HomeHelper authorizes to make an orderly reservation for the necessary departments and also fills as a stage for the advancement of the various departments accessible. The finer details of departments that include costs, name of home partners, and contact numbers are likewise shown on the advanced page so that the general community can choose which departments meet their models. This app is expected to create an ecosystem for holistic critical development for these freelance people and benefit an audience that really needs help with family chores or home maintenance. © 2021 IEEE.

关键词： Global positioning system

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An Efficient Cancer Classification Model Using Deep Neural Network with Arithmetic Optimization Algorithm-Based Optimal Gene Selection

An Efficient Cancer Classification Model Using Deep Neural N...

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International conference of Electronics, Communication and Aerospace technology (ICECA)

作者： Shyamala Gowri B S. Anu H Nair K. P. Sanal Kumar S. Kamalakkannan Department of Computer Science and Engineering Annamalai University Department of CSE Annamalai University Department of CS R V Government Arts College Chengalpattu Department of Information Technology School of Computing Sciences VISTAS

Cancers are the most disastrous and inevitable ailment that occurs in individuals. Due to the hazardous effects of cancer, people get at death in very early age. In today's date, cancer is categorized into many types, which are affected by the external and internal parts of the body. In general, cancers are caused by the growth of abnormal tissues where cancer originates and it is gradually spread to other parts. Therefore, the medical industry struggles to detect the different types of cancer disorders without any loss of people. Hence, the automated detection system is implemented to predict cancer in its early stages to prevent the people gets worsening. Normally, the collection of individual data is another challenging concern. Several methods have been implemented yet they exist with constraints to provide better results. Machine learning models are also used, but it does not tackle the big data collection process and also fail to obtain the relevant features. Henceforth, the deep learning model has emerged for various processes like prediction, classification, and recognition. So, a new and improved classification framework for classifying cancer is executed in this paper. At first, the data is gathered from the benchmark database. From the data, the genes are optimally selected using an Improved Arithmetic Optimization Algorithm (IAOA). Then, the optimally chosen genes are given as input to the “Optimized Deep Neural Network (ODNN)” for classification. The constraints in the DNN framework are optimized by the improved AOA. From the DNN, the classified output is obtained. Various experimentations are carried out by contrasting the developed optimization algorithm enhanced DNN model to verify the efficient working of the suggested cancer classification model. Throughout the result analysis, the accuracy and precision rate of the designed method is 93.42% and 9363% for all datasets.

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Multi-sensor Multi-Bernoulli target tracking algorithm based on measurement fusion 7

Multi-sensor Multi-Bernoulli target tracking algorithm based...

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7th International Conference on information science and Control Engineering, ICISCE 2020

作者： Cai, Ruhua Fan, Xiangting Wu, Sunyong Zou, Baohong Wang, Li Guilin University of Electronic Technology School of Mathematics and Computing Science Guilin China Guilin University of Electronic Technology Guangxi Information Science Experiment Center School of Mathematics and Computing Science Guilin China

ISBN: (纸本)9781728164069

This paper proposes a multi-sensor measurement fusion multi-Bernoulli filter to solve the problem of multi-target tracking. A greedy measurement fusion partitioning mechanism based on the multi-Bernoulli filter (MeMBer) is proposed. First, simultaneous selection and fusion of the measurement of different sensors are carried out. Then, the selection of the subsets and the operation of the partition are carried out through the greedy algorithm. In the updating stage of multi-Bernoulli filter, the multi-Bernoulli posterior is updated by using the measurement set after quasi-partitions on the basis of MeMBer filter. Simulation results show that the proposed filtering algorithm can estimate the number and state of multiple targets stably and effectively, and the OSPA error of tracking results is smaller and the cardinality estimation is more accurate than multi-sensor greedy multi-Bernoulli filter. © 2020 IEEE.

关键词： Clutter (information theory)

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Megavoltage Computed Tomography (MVCT) Imaging Quality Improvement via Convolutional Neural Network

Megavoltage Computed Tomography (MVCT) Imaging Quality Impro...

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International Conference on Image, Vision and Intelligent Systems, ICIVIS 2021

作者： Zhao, Zengjing Dong, Jiwen Niu, Sijie Zhang, Yan Zhu, Jian School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network-Based Intelligent Computing Jinan250022 China Department of Radiation Oncology Physics and Technology Shandong Cancer Hospital affiliated to Shandong First Medical University Jinan250022 China

ISBN: (纸本)9789811669620

Clinical application of Megavoltage Computed Tomography (MVCT) is limited to three-dimensional visualization and position of the patient. The noise and low soft tissue contrast of MVCT make it difficult to offer accurate target volume delineation for adaptive radiotherapy and accurate image registration for image-guided radiotherapy, resulting in the less clinical application prospect of MVCT. In this work, we developed a deep learning-based approach to improve the MVCT image quality, so as to learn a mapping between MVCT images and paired planning kilovoltage CT (KVCT) images. The root mean squared error (RMSE), structural similarity (SSIM), and peak signal-to-noise ratio (PSNR) were used to quantify. Specifically, the PSNR and SSIM of the processed images improved to 27.94 ± 1.64 dB and 0.93 ± 0.01 compared with 24.67 ± 1.16 dB and 0.88 ± 0.02 in the original MVCT images, and the RMSE decreased from 23.5 ± 3.14HU to 16.34 ± 3.39HU. The processed images were obtained using the proposed method with less noise and higher soft tissue contrast in comparison with machine learning-based method. The processed MVCT increases the feasibility of depicting the anatomical delineation of tumor and organs at risk, and therefore enables the promising application of image-guided and adaptive helical TomoTherapy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolution

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TOTAL: Multi-Corners Timing Optimization Based on Transfer and Active Learning 23

TOTAL: Multi-Corners Timing Optimization Based on Transfer a...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Wei W. Xing Zheng Xing Rongqi Lu Zhelong Wang Ning Xu Yuanqing Cheng Weisheng Zhao School of Integrated Circuit Science and Engineering Beihang University Beijing China Graphics & Computing Department Rockchip Electronics Co. Ltd Fuzhou China College of Physics and Electronic Engineering Sichuan Normal University Sichuan China School of Information Engineering Wuhan University of Technology Wuhan China

ISBN: (纸本)9798350323481

In modern advanced integrated circuit design, a design normally needs to be progressively optimized until the static timing analysis (STA) of full process corners meets the timing constraints. To improve efficiency, using machine learning to predict the path timings directly in order to reduce the extensive time-consuming SPICE simulations has become a promising technique to approach fast design closure. However, current methods lack both flexibility and reliability to be used in a practical industrial environment. To resolve these challenges, we propose TOTAL, which is constructed using a generalized linear model with latent features to effectively capture knowledge transferred from previous designs and delivers state-of-the-art (SOTA) prediction accuracy that is up to 6.6x improvement over the competitors in terms of mean absolute error (MAE). Most importantly, TOTAL is equipped with a Bayesian decision strategy to actively update uncertain predictions and deliver reliable predictions with accuracy close to 100%, pushing the frontier of the machine-learning-based STA for practical implementation.

关键词： static timing analysis

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Real-time and Downtime-tolerant Fault Diagnosis for Railway Turnout Machines (RTMs) Empowered with Cloud-Edge Pipeline Parallelism

arXiv

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arXiv 2024年

作者： Wu, Fan Bilal, Muhammad Xiang, Haolong Wang, Heng Yu, Jinjun Xu, Xiaolong The School of Software Nanjing University of Information Science and Technology Nanjing210044 China The School of Computing and Communications Lancaster University Lancaster BailriggLA1 4WA United Kingdom The NRIET Industrial Co. Ltd. Nanjing211106 China

Railway Turnout Machines (RTMs) are mission-critical components of the railway transportation infrastructure, responsible for directing trains onto desired tracks. Due to frequent operations and exposure to harsh environments, RTMs are susceptible to failures and can potentially pose significant safety hazards. For safety assurance applications, especially in early-warning scenarios, RTM faults are expected to be detected as early as possible on a continuous 7x24 basis. However, limited emphasis has been placed on distributed model inference frameworks that can meet the inference latency and reliability requirements of such mission-critical fault diagnosis systems, as well as the adaptation of diagnosis models within distributed architectures. This has hindered the practical application of current AI-driven RTM monitoring solutions in industrial settings, where single points of failure can render the entire service unavailable due to standalone deployment, and inference time can exceed acceptable limits when dealing with complex models or high data volumes. In this paper, an edge-cloud collaborative early-warning system is proposed to enable real-time and downtime-tolerant fault diagnosis of RTMs, providing a new paradigm for the deployment of models in safety-critical scenarios. Firstly, a modular fault diagnosis model is designed specifically for distributed deployment, which utilizes a hierarchical architecture consisting of the prior knowledge module, subordinate classifiers, and a fusion layer for enhanced accuracy and parallelism. Then, a cloud-edge collaborative framework leveraging pipeline parallelism, namely CEC-PA, is developed to minimize the overhead resulting from distributed task execution and context exchange by strategically partitioning and offloading model components across cloud and edge. Additionally, an election consensus mechanism is implemented within CEC-PA to ensure system robustness during coordinator node downtime. Comparative experiments

关键词： Railroad transportation

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Localization of Image Splicing Under Segment Anything Model With Integrated Compression and Edge Artifacts

Localization of Image Splicing Under Segment Anything Model ...

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IEEE International Conference on Image Processing

作者： Ruhao Zhao Xian Zhong Liang Liao Wenxuan Liu Wenxin Huang Zheng Wang School of Computer Science and Artificial Intelligence Hubei Key Laboratory of Transportation Internet of Things Wuhan University of Technology School of Electrical and Electronic Engineering Rapid-Rich Object Search Lab Nanyang Technological University College of Computing and Data Science Nanyang Technological University School of Computer Science and Information Engineering Hubei University School of Computer Science National Engineering Research Center for Multimedia Software Wuhan University

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

The localization of image splicing involves identifying pixels in an image that have been spliced from other images, necessitating the discernment of splicing features. Despite significant advancements driven by the rise of social media and deep learning, existing methods exhibit limitations, often neglecting the integration of coarse and precise features and lacking the ability to understand objects. This leads to erroneous predictions in identifying spliced regions. This paper proposes Segment Anything Model with Integrated Compression and Edge artifacts (SAM-ICE) for the localization of image splicing, addressing these limitations by fusing forged edge features and compression artifact features. Leveraging SAM’s object understanding ability, our method identifies spliced regions using the fused features as guidance. Specifically, we employ Edge Artifact Extractor (EAE) to extract fine high-frequency edge splicing features and Compression Artifact Extractor (CAE) to extract coarse compression artifact features. By combining these features, our method utilizes coarse-fine features to accurately pinpoint the spliced portions of the image. Experimental results demonstrate the superior accuracy, robustness, and generalizability of our method compared to the state-of-the-arts.

关键词： Location awareness Image segmentation Image coding Social networking (online) Image edge detection Splicing Feature extraction

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Dimension-reduced KRnet maps for high-dimensional Bayesian inverse problems

arXiv

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arXiv 2023年

作者： Feng, Yani Tang, Kejun Wan, Xiaoliang Liao, Qifeng School of Information Science and Technology ShanghaiTech University Shanghai201210 China Changsha Institute for Computing and Digital Economy Peking University Changsha410205 China Department of Mathematics Center for Computation and Technology Louisiana State University Baton Rouge70803 United States

We present a dimension-reduced KRnet map approach (DR-KRnet) for high-dimensional Bayesian inverse problems, which is based on an explicit construction of a map that pushes forward the prior measure to the posterior measure in the latent space. Our approach consists of two main components: data-driven VAE prior and density approximation of the posterior of the latent variable. In reality, it may not be trivial to initialize a prior distribution that is consistent with available prior data;in other words, the complex prior information is often beyond simple hand-crafted priors. We employ variational autoencoder (VAE) to approximate the underlying distribution of the prior dataset, which is achieved through a latent variable and a decoder. Using the decoder provided by the VAE prior, we reformulate the problem in a low-dimensional latent space. In particular, we seek an invertible transport map given by KRnet to approximate the posterior distribution of the latent variable. Moreover, an efficient physics-constrained surrogate model without any labeled data is constructed to reduce the computational cost of solving both forward and adjoint problems involved in likelihood computation. With numerical experiments, we demonstrate the accuracy and efficiency of DR-KRnet for high-dimensional Bayesian inverse problems. Copyright © 2023, The Authors. All rights reserved.

关键词： Inverse problems

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Exploring Data Geometry for Continual Learning

Exploring Data Geometry for Continual Learning

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhi Gao Chen Xu Feng Li Yunde Jia Mehrtash Harandi Yuwei Wu Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China Department of Electrical and Computer Systems Eng. Monash University and Data61 Australia

Continual learning aims to efficiently learn from a non-stationary stream of data while avoiding forgetting the knowledge of old data. In many practical applications, data complies with non-Euclidean geometry. As such, the commonly used Euclidean space cannot gracefully capture non-Euclidean geometric structures of data, leading to in-ferior results. In this paper, we study continual learning from a novel perspective by exploring data geometry for the non-stationary stream of data. Our method dynamically expands the geometry of the underlying space to match growing geometric structures induced by new data, and pre-vents forgetting by keeping geometric structures of old data into account. In doing so, making use of the mixed cur-vature space, we propose an incremental search scheme, through which the growing geometric structures are en-coded. Then, we introduce an angular-regularization loss and a neighbor-robustness loss to train the model, capa-ble of penalizing the change of global geometric structures and local geometric structures. Experiments show that our method achieves better performance than baseline methods designed in Euclidean space.

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Energy-Efficient Joint Resource Allocation in 5g Hetnet Using Multi-Agent Parameterized Deep Reinforcement Learning

SSRN

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SSRN 2023年

作者： Mughees, Amna Tahir, Mohammad Sheikh, Muhammad Aman Amphawan, Angela Meng, Yap Kian Ahad, Abdul Chamran, Kazem Department of Computing and Information Systems Sunway University Selangor47500 Malaysia Department of Computing University of Turku Turku20014 Finland School of Technologies Cardiff Metropolitan University CardiffCF5 2YB United Kingdom Department of Science & Technology University of Management and Technology Sialkot51040 Pakistan

Ultra-dense small cells are a promising technique to improve the capacity and throughput of future wireless networks. However, user association and power allocation in heterogeneous networks is complicated by the ultra-dense deployment of small cells resulting in non-convex and combinatorial problem. Conventionally, machine learning techniques are applied to the joint optimisation problem, which has different action spaces. Gauging the continuous spaces to discrete spaces results in the loss of granularity due to discretization (e.g. potential power values in power allocation). Due to its hybrid action space, it is sub-optimal to solve joint user association (discrete spaces) and power allocation (continuous spaces) problems by applying traditional machine learning approaches. This work proposes a Multi-Agent Parameterized Deep Reinforcement Learning (MA-PDRL) approach to address the joint user association and power allocation problem efficiently. According to simulation results, the proposed multi-agent PDRL performs better in energy efficiency and QoS satisfaction than WMMSE, game theory, Q-learning, and DRL techniques. © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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