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21st International Conference on New Trends in Intelligent software Methodologies, Tools and Techniques, SoMeT 2022

作者： Musa, Aminu Hassan, Mohammed Hamada, Mohamed Kakudi, Habeebah Adamu Amin, Md. Faizul Ibne Watanobe, Yutaka Department of Computer Science Federal University Dutse Nigeria Faculty of Computer Sci. & Info. Technology Bayero University Kano Nigeria Software Engineering Lab University of Aizu Japan Graduate School of Computer Science University of Aizu Japan Division of Information System University of Aizu Japan

ISBN: (纸本)9781643683164

Recent breakthroughs in computer vision have led to the invention of several intelligent systems in different sectors. In transportation, this advancement led to the possibility of proposing autonomous vehicles. This recent technology relies heavily on wireless sensors and Deep learning. For an autonomous vehicle to navigate safely on highways, the vehicle needs equipment to aid with detecting road anomalies such as potholes ahead of time. The massive improvement in computer vision models such as Deep Convolutional Neural networks (DCNN) or vision transformers (ViT) resulted in many success stories and tremendous breakthroughs in object detection tasks;this enabled the use of such models in different application areas. But many of the reported results are theoretical and unrealistic in real-life. Usually, the nature of these models is extensive;they are trained on High-performance computers or cloud computing environments with GPUs, which challenge their usage on edge devices. However, to come up with a light model that can fit into embedded devices, the model size has to be reduced significantly so that the performance will not be affected. Therefore, this paper proposes a lightweight model of pothole detection for an embedded device. The model achieved a state-of-the-art accuracy of 98%, with the number of parameters reduced to more than 70% compared with a deep CNN model;the model can be trained and deployed on embedded devices such as smartphones efficiently. © 2022 The authors and IOS Press. All rights reserved.

关键词： Autonomous vehicles

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Interference cancellation in RIS-assisted FD relay transmission

Interference cancellation in RIS-assisted FD relay transmiss...

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International Conference on Ubiquitous and Future Networks, ICUFN

作者： Donghyeon Kim Wooyeol Choi Chanjun Chun Yohan Kim Yonggang Kim AI & Robotics Tech Lab (ART Lab) Department of Computer Engineering Chosun University Department of Software Dongseo University Division of Computer Science and Engineering Kongju National University

Reconfigurable intelligent surface (RIS) is one of the promising technology for the next-generation wireless networks. The RIS reflects the received signal with phase shift introduced by reflecting elements without analog to digital conversion. In relay transmission scenario, RIS could be exploited to enhance the network coverage and improve transmission performance. In this paper, we consider RIS-assisted full-duplex (FD) relay transmission scenario. In the considered scenario, RIS and FD relay node performs relay transmission. Although the beamformed signal could be generated by phase shift of RIS, the interference from RIS to FD relay node may degrade the throughput performance. The proposed method cancels both of interference from RIS and self-interference at the FD relay node by estimating interference channels in the preparation phase before data transmission.

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Report-Guided Cross-Modal Representation Learning for Predicting EGFR Mutations by Whole Slide Image

Report-Guided Cross-Modal Representation Learning for Predic...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Qiao, Qi Shi, Jun Jiang, Zhiguo Wang, Wei Wu, Haibo Zheng, Yushan Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China Hefei University of Technology School of Software Hefei230601 China Beihang University Image Processing Center School of Astronautics Beijing102206 China University of Science and Technology of China Division of Life Sciences and Medicine Department of Pathology First Affiliated Hospital of Ustc Hefei230036 China University of Science and Technology of China Intelligent Pathology Institute Division of Life Sciences and Medicine Hefei230036 China Beihang University School of Engineering Medicine Beijing Advanced Innovation Center for Biomedical Engineering Beijing100191 China

ISBN: (纸本)9798350386226

Traditional PCR/NGS-based multigene panel testing is time-consuming and costly. Predicting EGFR mutations directly from H&E stained whole slide images (WSIs) can alleviate these limitations. Furthermore, histopathological reports contain valuable textual information that correlates with tissue areas in WSIs. However, recent research mainly analyses EGFR mutation status only from a single modality, ignoring rich information contained in reports. In this paper, we propose a report-guided cross-modal representation learning method for predicting EGFR mutations by WSIs. Specifically, we reconstruct report-level embeddings through exploring intrinsic relationships between diagnostic words in histopathological reports and tissue areas in WSIs. Finally, reconstructed histopathological report embedding and aggregated WSI embedding are fused for final prediction. More importantly, molecular testing report is also introduced as prior supervision information at the training stage to guarantee semantic consistency of fused feature and molecular report embedding. We evaluate our method on the TCGA-EGFR public benchmark dataset and an in-house clinical dataset (USTC-EGFR). Experimental results demonstrate that our method outperforms existing approaches in EGFR mutation prediction, highlighting the benefits of cross-modal learning in enhancing feature representational ability. The code is available at https://***/HFUT-miaLab/RCRL. © 2024 IEEE.

关键词： Supervised learning

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Adaptive Sampling for Efficient Softmax Approximation 38

Adaptive Sampling for Efficient Softmax Approximation

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Baharav, Tavor Z. Kang, Ryan Sullivan, Colin Tiwari, Mo Luxenberg, Eric Tse, David Pilanci, Mert Eric and Wendy Schmidt Center Broad Institute CambridgeMA02142 United States Department of Computer Science Stanford University StanfordCA94305 United States AI Division Software Engineering Institute PittsburghPA15213 United States Gridmatic CupertinoCA95014 United States Department of Electrical Engineering Stanford University StanfordCA94305 United States

The softmax function is ubiquitous in machine learning and optimization applications. Computing the full softmax evaluation of a matrix-vector product can be computationally expensive in high-dimensional settings. In many applications, however, it is sufficient to calculate only the top few outputs of the softmax function. In this work, we present an algorithm, dubbed AdaptiveSoftmax, that adaptively computes the top k softmax values more efficiently than the full softmax computation, with probabilistic guarantees. We demonstrate the sample efficiency improvements afforded by AdaptiveSoftmax on real and synthetic data to corroborate our theoretical results. AdaptiveSoftmax yields > 10x gain over full softmax computation on most datasets, yielding up to 30x improvement for Mistral7B evaluated on the Wikitext dataset. The adaptive method we propose for estimating the partition function (the softmax denominator) is of independent interest and can be used in other applications such as kernel density estimation. © 2024 Neural information processing systems foundation. All rights reserved.

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Improving WiFi Fingerprint Database With Irregular Distribution of Reference Points Using Interpolation Methods

Improving WiFi Fingerprint Database With Irregular Distribut...

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2024 International Conference on Advanced Technologies for Communications, ATC 2024

作者： Vu-Thanh, Trung He, Jing Duong-Bao, Ninh Lee, Seon-Woo Thi, Luong Nguyen Nguyen-Huu, Khanh College of Computer Science and Electronic Engineering Hunan University Changsha China Dalat University Faculty of Mathematics and Informatics Dalat Viet Nam Hallym University Division of Software Chuncheon Korea Republic of Dalat University Faculty of Information Technology Dalat Viet Nam Dalat University Department of Electronics and Telecommunications Dalat Viet Nam

ISBN: (纸本)9798350353983

Using WiFi Fingerprinting for indoor positioning is becoming increasingly popular due to its cost-effectiveness. However, to ensure the system can operate with high accuracy and stability, this technique requires a burden of data collection tasks from a dense distribution of reference points (RPs). To decrease the cost of data collection, as well as to improve the WiFi fingerprint database with irregular distribution of RPs, this study applies and compares different interpolation methods. Evaluating these methods in a public dataset including multiple sub-datasets with irregular distributions of RPs, the results highlight the Linear Barycentric interpolation (LB) as the most favorable method due to its low complexity and stable performance. The LB-based fingerprint database achieves an average interpolation error of less than 3 dBm compared to the original one. When applying the interpolated database to positioning tasks, it can help reduce acquisition costs by up to 50% while improving positioning accuracy by up to 11%. The study reveals the feasibility of interpolation in optimizing WiFi fingerprint data collection, especially in large and complex environments where regular distribution of RPs cannot be deployed. © 2024 IEEE.

关键词： Interpolation

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Robust Length of Stay Prediction Model for Indoor Patients

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computers, Materials & Continua 2022年第3期70卷 5519-5536页

作者： Ayesha Siddiqa Syed Abbas Zilqurnain Naqvi Muhammad Ahsan Allah Ditta Hani Alquhayz M.A.Khan Muhammad Adnan Khan Department of Mechatronics and Control Engineering University of Engineering and TechnologyLahore54000Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Department of Computer Science and Information College of Science in ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea

Due to unforeseen climate change,complicated chronic diseases,and mutation of viruses’hospital administration’s top challenge is to know about the Length of stay(LOS)of different diseased patients in the *** management does not exactly know when the existing patient leaves the hospital;this information could be crucial for hospital *** could allow them to take more patients for *** a result,hospitals face many problems managing available resources and new patients in getting entries for their prompt ***,a robust model needs to be designed to help hospital administration predict patients’LOS to resolve these *** this purpose,a very large-sized data(more than 2.3 million patients’data)related to New-York Hospitals patients and containing information about a wide range of diseases including Bone-Marrow,Tuberculosis,Intestinal Transplant,Mental illness,Leukaemia,Spinal cord injury,Trauma,Rehabilitation,Kidney and Alcoholic Patients,HIV Patients,Malignant Breast disorder,Asthma,Respiratory distress syndrome,*** been analyzed to predict the *** selected six Machine learning(ML)models named:Multiple linear regression(MLR),Lasso regression(LR),Ridge regression(RR),Decision tree regression(DTR),Extreme gradient boosting regression(XGBR),and Random Forest regression(RFR).The selected models’predictive performance was checked using R square andMean square error(MSE)as the performance evaluation *** results revealed the superior predictive performance of the RFRmodel,both in terms of RS score(92%)and MSE score(5),among all selected *** Exploratory data analysis(EDA),we conclude that maximumstay was between 0 to 5 days with the meantime of each patient 5.3 days and more than 50 years old patients spent more days in the *** on the average LOS,results revealed that the patients with diagnoses related to birth complications spent more days in the hospital than other *** finding coul

关键词： Length of stay machine learning robust model random forest regression

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Optimal Resource Allocation for NOMA-Assisted Backscatter Communication in 6G Internet of Vehicles Networks

Optimal Resource Allocation for NOMA-Assisted Backscatter Co...

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International Conference on Information Networking

作者： Van Dat Tuong Dongwan Kim Eui-Jik Kim Jeongyeup Paek Sungrae Cho School of Computer Science and Engineering Chung-Ang University Seoul Republic of Korea Department of Electronic Engineering Dong-A University Busan Republic of Korea Division of Software Hallym University Chuncheon Republic of Korea

ISBN: (数字)9798350330946

ISBN: (纸本)9798350330953

Non-orthogonal multiple access (NOMA) is considered as an emerging technology to improve the spectral efficiency performance for 6G networks. Ambient backscatter communication (BackCom) is another promising solution owing to its advantages in increasing spectral and power efficiency. The investigation of integrating NOMA with BackCom is conducted to enhance the connectivity of the emerging 5G low-powered Internet-of-Vehicles (IoVs) and future 6G transportation systems. This study proposes an optimal resource allocation scheme for the NOMA-assisted BackCom IoV network. Specifically, multiple Road-Side Units (RSUs) are considered sending superimposed signals to the subscribed IoVs in the downlink NOMA. BackCom tags are considered to transmit data symbols to nearby IoVs by reflecting the signals of RSUs. Thus, we aim to maximize the total data rate performance of the NOMA-assisted BackCom IoV network. A joint problem that simultaneously optimizes the IoV clustering, IoV power allocation, and reflection coefficients of BackCom tags is formulated. To solve the problems efficiently, multiple subproblems are decomposed that can be solved using CVX optimization tool. To validate the performance of the proposed method, we compare it with those of state-of-the-art benchmark schemes. Numerical results will be provided to show the superiority of the proposed framework.

关键词： 6G mobile communication NOMA Transportation Symbols Benchmark testing Reflection coefficient Resource management

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SCS: A Secure Cloud Storage Framework with Enhanced Integrity and Auditability Using Consortium Blockchain System

SCS: A Secure Cloud Storage Framework with Enhanced Integrit...

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IEEE International Conference on Smart Internet of Things (SmartIoT)

作者： Aguru Aswani Devi Erukala Suresh Babu Mekala Srinivasa Rao Rajesh Kaluri Thippa Reddy Gadekallu Department of Computer Science & Engineering SRM University AP Amaravati Andhra Pradesh India Department of Computer Science & Engineering National Institute of Technology Warangal India Department of Computer Science & Engineering Lakireddy Bali Reddy College of Engineering India Department of Software and Systems Engineering Vellore Institute of Technology Vellore India The College of Mathematics and Computer Science Zhejiang A&F University Hangzhou China Division of Research and Development Lovely Professional University Phagwara India

ISBN: (数字)9798350366440

ISBN: (纸本)9798350366457

The widespread adoption of cloud storage enables users to remotely access resources through a self-service model. Utilizing pay-per-use storage services provided by cloud service providers (CSPs) requires users to commit financially to their resources. This paper introduces a Secure Cloud Storage (SCS) framework, offering a secure architecture for cloud storage using a consortium blockchain network to address trust issues. This framework substitutes the third-party auditor with peers of a consortium blockchain network, which handles the role of data storage and verification. Storage space is divided into uncommitted and committed segments. Uncommitted storage is used for storing unverified documents, while committed storage is reserved for documents that have been validated through a consensus mechanism. In contrast, committed storage is des-ignated for the storage of committed documents. Documents validated by a consensus threshold of peer nodes are moved from uncommitted to committed storage. The implementation of the SCS framework is conducted using Hyperledger Fabric, a modular blockchain platform optimized for permissioned networks. The security analysis demonstrates that SCS effectively protects cloud storage against attacks, including unauthorized access attacks, data integrity attacks, and malicious server attacks, while maintaining data integrity and auditability. The performance evaluation shows that document upload and retrieval times, block acceptance, execution times, and latency are all improved compared to state-of-the-art cloud storage techniques.

关键词： Performance evaluation Cloud computing Data integrity Memory Peer-to-peer computing Servers Security Internet of Things Secure storage Self-service

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Comparative Analysis of Optimal ECG-Lead Subsets for Arrhythmia/ECG Pattern Classification: Deep Learning Versus Conventional Methods

SSRN

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SSRN 2024年

作者： Reznichenko, Serhii Whitaker, John Ni, Zixuan Zhou, Shijie The Department of Computer Science and Software Engineering Miami University Oxford45056 OH United States Division of Biomedical Engineering and Imaging Sciences College of Computer Science and Technology Zhejiang University Hangzhou China The Department of Chemical Paper and Biomedical Engineering Miami University Oxford45056 OH United States

Background:Deep Learning methods have been constantly growing in popularity. Prior to Deep Learning methods, Conventional Machine Learning has been effectively used for the classification of heart arrhythmias/ECG patterns. However, there has limited research into the potential benefits of Deep Learning methods over conventional Machine Learning. Given that Deep Learning often demands larger datasets to achieve robust classification results, using conventional techniques could yield superior accuracy. Additionally, the classification of heart arrhythmias/ECG patterns is often dependent on specific ECG leads for acccurate classification. It is unknown how Deep Learning and conventional Machine Learning methods perform on reduced subsets of ECG ***:The goal of the study is to compare the performance of a Deep Learning method to a conventional Machine Learning method to classify 8 different arrhythmias/ECG patterns using subsets of a full 12-lead ECG .Methods:We used a public dataset from the PhysioNet Cardiology Challenge 2020. The research involved the utilization of both Deep Learning and Conventional Machine Learning methods. For the Deep Learning method, we trained a CNN classifier and extracted 32 features for each ECG lead, which were then used in a feedforward neural network. For the conventional Machine Learning method, we employed a Random Forest classifier that operated on manually extracted features from the ECG signals. The optimal subsets of ECG leads were identified using recursive feature elimination for both of the methods. This process involves training a classifier, evaluating its performance, identifying the least important lead, eliminating it, and then iteratively repeating the procedure. We determined lead importance using Random Forest feature importance scores for the conventional Machine Learning method and used SHAP values for Deep Learning. Finally, the subset of ECG leads that was statistically significant (p-value Results:We id

关键词： Electrocardiograms

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Efficient Privacy-Preserving ML for IoT: Cluster-Based Split Federated Learning Scheme for Non-IID Data 7

Efficient Privacy-Preserving ML for IoT: Cluster-Based Split...

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7th Cyber Security in Networking Conference, CSNet 2023

作者： Arafeh, Mohamad Wazzeh, Mohamad Ould-Slimane, Hakima Talhi, Chamseddine Mourad, Azzam Otrok, Hadi Department of Software and It Engineering Montreal Canada Department of Mathematics and Computer Science Canada New York University Division of Science Abu Dhabi United Arab Emirates Cyber Security Systems and Applied Ai Research Center Lebanese American University Department of Csm Beirut Lebanon Khalifa University Department of Eecs Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350342871

This paper proposes a scheme addressing the challenges of integrating privacy-preserving distributed machine learning in the Internet of Things (IoT) context while improving the efficiency of the learning process and accelerating the convergence of the model. Specifically, it addresses the presence of non-independent and identically distributed (Non-IID) data and device heterogeneity, which occurs during parallel training and are common characteristics of IoT environments. In this context, SplitFed is a privacy-preserving technique that combines Federated Learning (FL) and Split Learning (SL) to enable fast and privacy-preserving model training on IoT devices. Through parallelism, FL allows fast and parallel model training. Furthermore, SL enables weak devices to train models through model splitting. However, SplitFed suffers from the increasing convergence time due to the FL parallelism introducing Non-IID and stragglers issues. In this paper, we aim to address the aforementioned problems by proposing a clustering parallelization scheme. As such, similar clients are combined based on their weight divergence and considered a single FL unit addressing the Non-IID problem. Moreover, to improve time efficiency and overcome the stragglers' presence, we offer a second clustering stage that separates clients by efficiency and uses FedAsync for model aggregation. © 2023 IEEE.

关键词： Internet of things

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