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arXiv 2024年

作者： Park, Yu Min Hassan, Sheikh Salman Tun, Yan Kyaw Huh, Eui-Nam Saad, Walid Hong, Choong Seon Department of Computer Science and Engineering Kyung Hee University Gyeonggi-do Yongin-si17104 Korea Republic of Department of Electronic Systems Aalborg University A. C. Meyers Vænge 15 København2450 Denmark Bradley Department of Electrical and Computer Engineering Virginia Tech VA24061 United States

Non-orthogonal multiple access (NOMA) enables multiple users to share the same frequency band, and simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) provides 360-degree full-space coverage, optimizing both transmission and reflection for improved network performance and dynamic control of the indoor environment. However, deploying STAR-RIS indoors presents challenges in interference mitigation, power consumption, and real-time configuration. In this work, a novel network architecture utilizing multiple access points (APs), STAR-RISs, and NOMA is proposed for indoor communication. To address these, we formulate an optimization problem involving user assignment, access point (AP) beamforming, and STAR-RIS phase control. A decomposition approach is used to solve the complex problem efficiently, employing a many-to-one matching algorithm for user-AP assignment and K-means clustering for resource management. Additionally, multi-agent deep reinforcement learning (MADRL) is leveraged to optimize the control of the STAR-RIS. Within the proposed MADRL framework, a novel approach is introduced in which each decision variable acts as an independent agent, enabling collaborative learning and decision making. The MADRL framework is enhanced by incorporating convex approximation (CA), which accelerates policy learning through suboptimal solutions from successive convex approximation (SCA), leading to faster adaptation and convergence. Simulations demonstrate significant improvements in network utility compared to baseline approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Decision making

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IDENTIFICATION OF MOST SIGNIFICANT PARAMETER FOR OPTIMIZING THE PERFORMANCE OF RPL ROUTING PROTOCOL IN IoT USING TAGUCHI DESIGN OF EXPERIMENTS

arXiv

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arXiv 2023年

作者： Sanaboina, Chandra Sekhar Sanaboina, Pallamsetty Jawaharlal Nehru Technological University Andhra Pradesh Kakinada India Department of Computer Science and Systems Engineering Andhra University Andhra Pradesh Visakhapatnam India

Internet of Things (IoT) consists of a wide variety of devices with limited power sources. Due to the adhered reason, energy consumption is considered as one of the major challenges in the IoT environment. In this research article, an attempt is made to optimize the existing Routing Protocol (RPL) towards a green technology. It focuses on finding the most significant parameter in the RPL using Taguchi Design of Experiments. It emphasizes the effects of five input factors, such as Network Size, Mobility Speed, DIO_DOUBLING, DIO_MIN_INTERVAL, and Redundancy Constant on only one output parameter Power Consumption. The findings show that DIO_MIN_INTERVAL is the leading factor that has a significant effect on the power consumption in RPL. After determining the most significant factor that affects the power consumption, measures can be taken to optimize the performance of RPL by applying some optimization techniques. COOJA simulator is used to carry out the simulations required for this research article. © 2023, CC BY-NC-SA.

关键词： Design of experiments

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Novel 6D Pose Estimation for Pick-and-Place Tasks using Model-based Learning

Novel 6D Pose Estimation for Pick-and-Place Tasks using Mode...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Logesh G S. Vinoth Nagarajan Pitchandi A.G. Karthikeyan Computer Science and Business Systems Bannari Amman Institute of Technology Sathyamangalam India Department of Mechatronics Akshaya College of Engineering and Technology Coimbatore India Department of Mechatronics Bannari Amman Institute of Technology Sathyamangalam India School of Mechanical Engineering REVA University Bangalore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The paper discusses the problem of creating an autonomous SCARA robot that can precisely carry out pick-and-place operations in unstructured settings. The suggested method combines computer vision and deep learning algorithms to provide accurate 6D pose estimation and reliable object detection. Combining YOLOv8 for object detection, hand-eye calibration for precise transformation to robot coordinates, and Principal Component Analysis (PCA) for orientation estimation within the generated point cloud, a novel method for estimating object pose is presented. Monocular depth estimation techniques are used to extract depth information from RGB images to create point clouds. The results demonstrate the potential of combining advanced algorithms with user-friendly design for robotics automation, showing notable gains in pose estimation accuracy and task execution efficiency.

关键词： Point cloud compression Accuracy Service robots Robot kinematics Pose estimation Object detection Manuals Calibration Reliability Principal component analysis

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Predictive Prescription of Unit Commitment Decisions Under Net Load Uncertainty

Predictive Prescription of Unit Commitment Decisions Under N...

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Innovative Smart Grid Technologies (ISGT)

作者： Ogun Yurdakul Feng Qiu Sahin Albayrak Department of Electrical Engineering and Computer Science Technical University of Berlin Berlin Germany Energy Systems Division Argonne National Laboratory Lemont IL USA

To take unit commitment (UC) decisions under uncertain net load, most studies utilize a stochastic UC (SUC) model that adopts a one-size-fits-all representation of uncertainty. Disregarding contextual information such as weather forecasts and temporal information, these models are typically plagued by a poor out-of-sample performance. To effectively exploit contextual information, in this paper, we formulate a conditional SUC problem that is solved given a covariate observation. The presented problem relies on the true conditional distribution of net load and so cannot be solved in practice. To approximate its solution, we put forward a predictive prescription framework, which leverages a machine learning model to derive weights that are used in solving a reweighted sample average approximation problem. In contrast with existing predictive prescription frameworks, we manipulate the weights that the learning model delivers based on the specific dataset, present a method to select pertinent covariates, and tune the hyperparameters of the framework based on the out-of-sample cost of its policies. We conduct extensive numerical studies, which lay out the relative merits of the framework vis-à-vis various benchmarks.

关键词： Radio frequency Training Costs Uncertainty Stochastic processes Weather forecasting Predictive models

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International Conference of computer and Informatics engineering (IC2IE)

作者： Wandy Wandy Kusworo Adi Media Anugerah Ayu Doctoral Program of Information Systems School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Science Faculty of Engineering and Technology Sampoerna University South Jakarta Indonesia

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

Full-marathon and Half-marathon distances are categorized as road running. Full-marathon running is becoming increasingly popular, and Half-marathon is increasing worldwide in both sexes and all age groups. Some aspects might relate to Full-marathon and Half-marathon running performance during training and races. Technology also plays an essential role in supporting runners and running races. Technology like artificial intelligence (AI) now supports the running athlete, not only predicting performance and results. It can also be used later to help the coach generate training programs for the athlete. This research aimed to find many aspects of marathons and performance and analyze them to see if artificial intelligence could later support them. It used secondary data and a systematic literature review proposed by Kitchenham. Out of the 58 articles, 21 of them (36.21%) received a score of 1 from Q1. Additionally, 19 articles (32.76%) received a score of 1 from both Q2 and Q3. Among the 58 articles, 9 (15.52%) received a total score of 3, with all three Q1, Q2, and Q3 scores being 1. This indicates that artificial intelligence will likely support the content of these nine articles. Several factors were also discovered to be connected to marathons and athletic performance. These findings suggested that additional investigation into marathons and performance, later backed by artificial intelligence, remained pertinent and essential.

关键词： Training Roads Artificial intelligence Informatics

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A hybrid approach of blockchain and semantic web technologies to validating learning outcomes in accordance with legal constraints

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International Journal of Information Technology (Singapore) 2022年第6期14卷 2893-2901页

作者： Nguyen, Minh Duc Nguyen-Ngoc, Thuy Nguyen-Dinh, Cuong H. Le, Anh Phuong Department of Economic Information Systems University of Economics Hue University Hue Viet Nam Department of Information Technology University of Sciences Hue University Hue Viet Nam School of Engineering and Technology Hue University Hue Viet Nam Department of Computer Science University of Education Hue University Hue Viet Nam

In both academia and industry, the usage of fake certificates has led to misjudging learners and workers. Moreover, the digital form of certificate has made this problem more critical, especially in the educational system conforming to law. Various blockchain-based solutions against fake certificates have been proposed, however, the validation considering both of educational requirements and related legal regulations was not studied thoroughly. This paper proposes a hybrid method combining blockchain technology and semantic web technology for validating the learning data and verifying the learning outcomes with legal constraints that guarantees data transparency and semantic transparency. A semantic-blockchain multi-layer learning framework was constructed based on a three-layer foundation including the layers of learning-data, legal regulations and learning certificates. An experiment on a prototype of the proposed framework yielded promising result. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Blockchain Certificate validation Legal regulation Ontology Semantic web

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Graph Encoding-Enhanced Transformer for Drug Recommendation

Graph Encoding-Enhanced Transformer for Drug Recommendation

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IEEE International Conference on Big Data

作者： Xunsheng Cai Syauki Aulia Thamrin Arbee L.P. Chen Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering Asia University Taichung Taiwan

Doctors prescribe drugs for the patient with the objective of curing the patient. Some drugs cannot be consumed together since doing so may cause negative effects. This can be avoided by knowing the effects caused by consuming combinations of drugs. However, for complex cases of a patient, it can be difficult to decide the best combination of drugs. Therefore, automatic drug recommendation method was used to recommend drugs with minimal negative effects. It is performed by using a deep learning model which is trained on drug data. A graph called drug-drug interaction (DDI) is used to represent the drugs and effects of consuming one drug with other drugs. Additionally, information about the combination of drugs prescribed in the past for a patient is also important for drug recommendation. It can also be represented as a graph called drug concurrence relation (DCR). The DDI and DCR graphs can be input to the deep learning model through an encoding process. In this paper, we propose a graph encoding-enhanced transformer (GEET) to recommend drugs. The DDI and DCR graphs are encoded by using Graph Attention Network (GAT). The graph encoding model has multi-head attention, which makes the GEET model aware of the most important DDI and DCR from the graphs. Additionally, the encoding outputs are combined, and activation function and normalization methods are used to improve the performance. The model has been evaluated on the publicly available MIMIC-III dataset and has the best results on F1, Jaccard and PRAUC scores compared to the models proposed by the existing related research papers.

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A Dummy User Based Privacy Preserving Model for Online and Offline Crowd Mobility Monitoring Applications

SSRN

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SSRN 2024年

作者： Venkatesan, S. Göbel, Richard Tapas, Nachiket Rajendra, Yuvaraj Vyas, Om Prakash Shukla, Sandeep Kumar Vyas, Ranjana Department of Information Technology Indian Institute of Information Technology Uttar Pradesh Prayagraj211015 India Institute for Information Systems Hof University Alfons-Goppel-Platz 1 Germany Department of Computer Science and Engineering Indian Institute Technology Uttar Pradesh Kanpur208016 India Department of Computer Science and Engineering Chhattisgarh Swami Vivekanand Technical University Chhattisgarh Bhilai491107 India

In the context of smart cities, it is necessary to record and analyse crowd mobility for various reasons, such as providing efficient public services and preventing accidents. However, recording and tracking individuals’ movements can pose a threat to their privacy. Therefore, it is crucial to ensure the privacy of individuals while collecting and analysing their mobility data. Several related works in the literature attempt to ensure the privacy of individuals in monitoring applications by adding dummy data, noise, or creating duplicates in the trajectories. Our work proposes adding a dummy user to live movements to ensure privacy both online and offline, even when the source location is carried throughout the entire travel path. Additionally, it provides unbiased data for decision-making. The proposed model is compared with existing models to demonstrate its suitability for monitoring crowd mobility. A privacy measurement metric based on the crowds in an individual’s travel path, as well as possible trajectories of their travel hopsor levels, has been proposed. To simulate crowd mobility and analyze the proposed model, P2M2Sim was developed. The Privacy-Preserving Mobility Monitoring Simulator presents the mobility of individuals and their locations. The simulator also outputs privacy results to demonstrate the effectiveness of the proposed model. The output based on the proposed model is analyzed, and it is shown that the addition of dummy data does not impact decision-making in the mobility monitoring application. © 2024, The Authors. All rights reserved.

关键词： Trajectories

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Prediction of endometrial cancer MRI images based on deep learning and improved Bayesian extreme learning machine ensemble learning 5

Prediction of endometrial cancer MRI images based on deep le...

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5th International Conference on Frontiers Technology of Information and computer, ICFTIC 2023

作者： Wang, Ximiao Hu, Qinghui Dai, Xisheng Li, Peijun Chen, Yuan Guangxi University of Science and Technology Institute of Intelligent Systems and Control Guangxi Province Liuzhou City545006 China Guilin University of Aerospace Technology School of Computer Science and Engineering Guangxi Province Guilin City541004 China Jiangmen Central Hospital Department of Radiology Guangdong Province Jiangmen City529000 China Jiangmen Central Hospital Department of Gynecology Guangdong Province Jiangmen City529000 China

ISBN: (纸本)9798350309034

For the prediction of deep myometrial invasion (DMI) in endometrial cancer (EC), this study proposes an ensemble learning method which combines deep learning (DL) and improved Bayesian extreme learning machine (BELM). Firstly, the MRI images of endometrial cancer are preprocessed to meet the requirements. Secondly, the deep features relevant to the task are extracted from the images by using convolutional kernels. Finally, the bootstrap resampling method is employed to repeatedly sample multiple training subsets from the training set. The improved Bayesian extreme learning machine is used as the base classifier to train multiple independent sub-models. An ensemble learning classifier is constructed using an ensemble strategy to improve the prediction accuracy and stability of the model. Experimental results show that the proposed method achieves an AUC of 0.758 on the internal validation set and 0.740 on the external validation set, which demonstrat good generalization ability and stability of the proposed method. © 2023 IEEE.

关键词： Bayesian Extreme Learning Machine Deep Learning Deep Myometrial Invasion Endometrial Cancer Ensemble Learning

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Data poisoning: issues, challenges, and needs

Data poisoning: issues, challenges, and needs

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7th IET Smart Cities Symposium (SCS 2023)

作者： Mohammad Aljanabi Alaa Hamza Omran Maad M Mijwil Mostafa Abotaleb El-Sayed M. El-kenawy Sahar Yousif Mohammed Abdelhameed Ibrahim Department of computer College of Education Aliarqai university Baghdad Iraq University of information technology and communications Baghdad Iraq Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Science Department Computer Science & Information Technology College Anbar University Anbar Iraq Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

Data poisoning attacks, where adversaries manipulate training data to degrade model performance, are an emerging threat as machine learning becomes widely deployed in sensitive applications. This paper provides a comprehensive overview of data poisoning including attack techniques, adversary incentives, impacts on security and reliability, detection methods, defenses, and key research gaps. We examine label flipping, instance injection, backdoors, and other attack categories that enable malicious outcomes ranging from IP theft to accidents in autonomous systems. Promising detection approaches include statistical tests, robust learning, and forensics. However, significant challenges remain in translating academic defenses like adversarial training and sanitization into practical tools ready for operational use. With safety and trustworthiness at stake, more research on benchmarking evaluations, adaptive attacks, fundamental tradeoffs, and real-world deployment of defenses is urgently needed. Understanding vulnerabilities and developing resilient machine learning pipelines will only grow in importance as data integrity is fundamental to developing safe artificial intelligence.

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