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IEEE Conference on Vehicular Technology (VTC)

作者： Nektarios Moraitis Demosthenes Vouyioukas School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Dept. of Information and Communication Systems Engineering University of the Aegean Samos Greece

This paper presents the preliminary results of a propagation study at D-band based on deterministic modeling for an indoor line-of-sight (LOS) environment. The proposed channel model is based on multiple rays between a fixed transmitter and a moving receiver. The results reveal that the antenna characteristics significantly affect the propagation along the corridor, which is also reflected to the produced power delay profiles of the channel. Furthermore, the existence of side-corridors also influences the channel parameters, reducing the root-mean-square (rms) delay and angular spread values. At 12-m distance away from the transmitter the rms delay spread varies between 0.1 and 4.3 ns, whereas the azimuth and elevation rms angular spreads (AS) are in the range of 0.7°-12.4° and 2.1°-7.0°, respectively, depending on the selected scenario and antenna configuration.

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Iterative Motion Planning in Multi-agent systems with Opportunistic Communication under Disturbance

arXiv

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arXiv 2025年

作者： Thelasingha, Neelanga Julius, Agung Humann, James Dotterweich, James Dept. of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute NY United States James Humann and James Dotterweich are with DEVCOM Army Research Laboratory MD United States

— In complex multi-agent systems involving heterogeneous teams, uncertainty arises from numerous sources like environmental disturbances, model inaccuracies, and changing tasks. This causes planned trajectories to become infeasible, requiring replanning. Further, different communication architectures used in multi-agent systems give rise to asymmetric knowledge of planned trajectories across the agents. In such systems, replanning must be done in a communication-aware fashion. This paper establishes the conditions for synchronization and feasibility in epistemic planning scenarios introduced by opportunistic communication architectures. We also establish conditions on task satisfaction based on quantified recoverability of disturbances in an iterative planning scheme. We further validate these theoretical results experimentally in a UAV–UGV task assignment problem. © 2025, CC BY.

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Evolutionary Interest Representation Network for Click-Through Rate Prediction

Evolutionary Interest Representation Network for Click-Throu...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Zhou, Renjie Jin, Zhenming Wan, Jian Zhang, Jilin Guo, Xingyu Liu, Qiang Hangzhou Dianzi University Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education School of Computer Science and Technology Hangzhou China Hangzhou Dianzi University Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education Itmo Joint Institute Hangzhou China Zhejiang University of Science and Technology School of Information and Electronic Engineering Hangzhou China Zhejiang Police College Dept. of Computer and Information Technology Hangzhou China

ISBN: (纸本)9798350304855

The rapid development of the Internet has revo-lutionized our lives, providing us with an array of convenient services. However, this revolution has also led to the problem of data overload. Personalized recommendation services have been widely recognized as an effective solution to this problem by both the industrial and academic communities. Accurate prediction of click-through rate (CTR) is a critical task in personalized recommendation systems since it can improve the user's shopping experience, ultimately increasing revenue for the platform. To achieve accurate CTR prediction, it is crucial to capture user interests. While several methods for interest modeling exist, challenges related to user interest evolution and user behavior noise need further attention. This paper proposes a novel CTR prediction model named the Evolutionary Interest Representation Network (EIRN), which learns the evolving interest representation of users from their behaviors, profiles, and environmental attributes. In the model, we introduce an advertisement correlation graph decomposition layer and a noise filter layer to enhance and purify the user behavior representation. An interest extraction layer is designed to capture sequential correlations and common characteristics between user behaviors, representing their interests. Additionally, as user interests evolve over time and their importance changes, an attention-based interest evolution layer is designed to track the evolution of user interests. Our proposed method is validated using four public datasets and an industrial dataset, showing its superiority over existing CTR prediction methods. © 2023 IEEE.

关键词： Forecasting

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Dynamic Image Deblurring based on Crosshatch Attention Adversarial Network and Hybrid Loss

Dynamic Image Deblurring based on Crosshatch Attention Adver...

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International Conference on Advancements in Computational Sciences (ICACS)

作者： Ummama Aslam Arslan Shaukat Basim Azam Dept. of Computer and Software Engineering College of Electrical and Mechanical Engineering (CEME) National University of Sciences and Technology (NUST) Islamabad Pakistan Institute of Integrated and Intelligent Systems Griffith University Brisbane Australia

Image deblurring techniques that uses deep learning have shown great potential but due to low generalizability, noise immunity and the correlation among different pixels is not addressed in detail that results in unwanted artifact that appears in the deblurred image. To tackle this problem an end-to-end approach is proposed for the recovery of sharp image from blurred one without the estimation of blur kernel. A special type of attention module known as crosshatch attention is used after Residual Block of Generator model for removing noise and for the collection of correlation of different pixels in an image. Hybrid Loss function is defined which focus on different part of image and improve edges and texture details. The performance of the model for deblurring is measured on GoPro dataset. Our proposed model has slightly higher objective and subjective evaluation i-e PSNR, SSIM value and the visual results.

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Disclosure Privacy Information Social Media on TikTok 9

Disclosure Privacy Information Social Media on TikTok

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Syamsuar, Dedy Fahrezi, Fazri Favian, Ahnaf Irwansyah, Edy Irawan, Achmad Fakhri School of Information System Bina Nusantara University Faculty of Engineering Professional Engineer Program Dept. Information System Department Jakarta11480 Indonesia School of Information System Bina Nusantara University Information System Department Jakarta11480 Indonesia School of Information System Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

With the rapid development of technology, the use of social media by the public, especially among young people, is increasing. One of the social media platforms currently used by young people is the TikTok application. It is a video-based TikTok feature accompanied by music, writing, and pictures that are considered attractive, so teenagers like it to show their existence and self-disclosure. Therefore, this study aims to examine the intention of users to disclose their privacy. As the basis of the theory, this study deployed the privacy calculus theory, where the perceived benefits and perceived risks play crucial roles in the intention to disclose their privacy. The study successfully collected 369 responses. The empirical findings indicated that only the relationship between Perceived Personalization (PP) and Perceived Risk (PR) was not supported. However, all the remaining hypotheses were supported. These findings emphasize how several complicated variables influence social media users' intentions to disclose their privacy. When people feel in control of their privacy settings and see significant benefits, they are more likely to divulge personal information;nevertheless, perceived risks can greatly discourage users. © 2024 IEEE.

关键词： Differential privacy

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Evaluation of Benchmarking Pre-Trained CNN Model for Autonomous Vehicles Object Detection in Mixed Traffic 9

Evaluation of Benchmarking Pre-Trained CNN Model for Autonom...

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9th International Conference on ICT for Smart Society, ICISS 2022

作者： Afdhal, Afdhal Nasaruddin, Nasaruddin Fuadi, Zahrul Sugiarto, Sugiarto Riza, Hammam Saddami, Khairun Doctoral Program School of Engineering Universitas Syiah Kuala Banda Aceh Indonesia Universitas Syiah Kuala Dept. of Electrical and Computer Engineering Banda Aceh Indonesia Universitas Syiah Kuala Dept. of Mechanical and Industrial Engineering Banda Aceh Indonesia Universitas Syiah Kuala Department of Civil Engineering Banda Aceh Indonesia Jakarta Indonesia Universitas Syiah Kuala Banda Aceh Indonesia

ISBN: (数字)9781665471350

ISBN: (纸本)9781665471350

In the next few years, the new generation of Autonomous Vehicles (AVs) promises an advanced level of self-driving experiences. One of the most challenging topics in AVs development is the readiness of object detection models in complex urban environments. Mixed traffic is a complex urban environment that contains much uncertainty and is composed of heterogeneous objects. Therefore, this paper evaluates benchmarking the pre-trained CNN model for object detection in a mixed traffic environment. The evaluation is conducted for five modern algorithms and architecture of neural networks, including Faster RCNN, SSD, YOLOv3, YOLOv4, and EfficientDet. Then, we provide a new dataset in the mixed traffic environment under night conditions for more accurate object detection. Moreover, we conduct the simulation by considering the performance parameters that are recall, precision, and F measure. The performance of our dataset is also compared to the MS-COCO dataset. The result shows that the average precision value of Faster RCNN, SSD, YOLOv3, YOLOv4, and EfficientDet is 16.70%, 8.90%, 19.67%, 43.90%, and 55.56% respectively. It shows that YOLOv4 and EfficientDet provide better object detection accuracy than other CNN models. © 2022 IEEE.

关键词： Object detection

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Development of a Modified Dummy Gas Lift Valve for Subsurface dept.-Resolved Microbiological Sampling and 3D Mapping

Development of a Modified Dummy Gas Lift Valve for Subsurfac...

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2024 SPE Water Lifecycle Management Conference and Exhibition, WLMC 2024

作者： McElhinney, J.M.W.R. Gomes, J.S. Hasan, A. Dias, J. BME Department Khalifa University Abu Dhabi United Arab Emirates Centre for Membranes and Advanced Water Technology Arzanah Building Khalifa University SAN Campus Abu Dhabi United Arab Emirates Computer and Electrical Engineering Dept. Khalifa University Abu Dhabi United Arab Emirates KU Center for Autonomous Robotic Systems

ISBN: (纸本)9781959025320

Objectives/Scope: This study outlines the development and use of a modified dummy gas lift valve designed to obtain subsurface microbiological samples from a defined dept. series across a production well. An overview of the initial field pilot is presented wherein microbiological data from the modified valve will be contrasted with topside samples to evaluate the information yielded from sampling downhole biofilm communities in place of planktonic populations from the well head. Methods, Procedures, Process: In this approach, the modified dummy gas lift valve, preloaded topside with carbonate plugs and coupons, is deployed at various dept.s in the well's completion for downhole incubation. This modification allows production fluids to wet the payloads, enabling in situ biofilm colonization by reservoir communities across dept. and temperature gradients. Post retrieval using a slickline tool, sample DNA is extracted for metagenomic sequencing, assessing microbial composition and metabolic capabilities (including souring and MIC-related activities). Concurrent production fluid samples from the wellhead will be analyzed for comparison to evaluate any information gained from biofilm over planktonic population sampling. Results, Observations, Conclusions: Samples from the modified valve are expected to provide sufficient yields of DNA, uncontaminated by non-resident microbes from outside of the producer. Microbial data from downhole biofilms is anticipated to provide a more diverse community makeup than production fluid communities sampled at the wellhead, with a corresponding increase in problematic species associated with MIC and souring. Corrosion coupon analyses will enable conclusions regarding proactive decision-making enablement and extrapolations of 3D projections of microbiological communities between modified valves across the completion column will provide the relationship between in situ conditions, microbial colonization in the context of pore space and risk

关键词： Decision making

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Robust Heart Disease Prediction: A Hybrid Approach to Feature Selection and Model Building 4

Robust Heart Disease Prediction: A Hybrid Approach to Featur...

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4th International Conference on Ubiquitous Computing and Intelligent Information systems, ICUIS 2024

作者： Kodete, Chandra Shikhi Pasupuleti, Vikram Thuraka, Bharadwaj Sangaraju, Varun Varma Tirumanadham, N. S. Koti Mani Kumar Shariff, Vahiduddin School of Technology Eastern Illinois University CharlestonIL61920 United States School of Computer Science and Information Systems Northwest Missouri State University MaryvilleMO64468 United States University of the Cumberlands WilliamsburgKY4076 United States Sir C R Reddy College of Engineering Dept. of Cse Eluru India

ISBN: (纸本)9798331529635

This research work integrates advanced data preparation methods and advanced modelling approaches to offer a complete approach for creating a predictive model for heart disease. Data cleansing starts the procedure in order to fix disparities and manage missing values, thereby guaranteeing the dependability and accuracy of the dataset. By producing synthetic samples for the minority class, the Synthetic Minority Over-sampling Technique (SMOTE) solves class imbalances and improves the accuracy of the model to forecast minority class occurrences. The z-score approach helps to control outliers thereby preventing distortion in the study. Using a hybrid Filter-Recursive Feature Elimination (FRFE) approach-which combines filter and wrapper techniques to find the most pertinent features, therefore lowering multicollinearity and boosting model performance-feature selection is accomplished. Important characteristics found are age, kind of chest discomfort, maximal heart rate attained, ST depression brought on by exercise, and fluoroscopy-induced coloration of several major arteries. Using AdaBoost, an ensemble learning technique combining several weak classifiers to create a strong one, the improved dataset is utilised to develop a predictive model. The resultant model shows great accuracy (93%), precision (93%), recall (94%), and an amazing ROC AUC score (0.98), therefore highlighting its potential use in healthcare environments and heart disease prediction ability. © 2024 IEEE.

关键词： Feature Selection

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Joint Design of Spirograph Method and Reconfigurable Intelligent Surface for Future Communication systems

Joint Design of Spirograph Method and Reconfigurable Intelli...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Balqis Yafis Jane-Hwa Huang Chih-Min Yu Li-Chun Wang EECS Int’l Graduate Program National Yang Ming Chiao Tung University Hsinchu Taiwan Dept. of Electrical Engineering National Chi Nan University Taiwan Department of Information and Computer Engineering Chung Yuan Christian University Taiwan Dept. of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Reconfigurable Intelligent Surface (RIS) is a transformative technology using the passive beamforming capabilities to create the programmable wireless environments. However, the control overhead is a challenging issue. The system needs to deliver the high-precision reflection coefficients to the RIS controller, for adjusting the phase shifts of the numerous RIS elements. Therefore, we propose a Spirograph-based discrete beamdirection strategy, which selects a set of non-uniformly distributed beam directions based on the generic patterns generated by the Spirograph (SG). We evaluate the achievable data rate and the number of controlling bits of SG-based method. We compare the SG-based method, the ideal continuous phase-shift method, and the SISO method. Simulation results show that with the properly-selected discrete beam directions, the SG-based method can achieve a comparable data rate as the ideal method, while having a lower control overhead.

关键词： Wireless communication Quantization (signal) Array signal processing Simulation Design methodology Reconfigurable intelligent surfaces Reflection coefficient Global communication

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Virtual-Device-Based Policy Enforcement in Multi-Admin Smart Environments

Virtual-Device-Based Policy Enforcement in Multi-Admin Smart...

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IEEE International Conference on Cloud Networking (CLOUDNET)

作者： Yunping Fang Chenglong Fu Xiaojiang Du Dept. of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Dept. of Software and Information Systems Univ. of North Carolina at Charlotte Charlotte NC USA

The Matter standard, formerly known as Connected Home over IP, has emerged as the preferred choice for most smart home IoT vendors and service providers for the next-generation smart home IoT systems. It enhances interoperability across different smart home ecosystems and introduces the multi-admin feature, allowing a device to be commissioned and managed by multiple platforms simultaneously. While this standard offers considerable convenience, it also presents challenges for security policy enforcement. Policy enforcement has been highlighted in various studies as a crucial countermeasure against smart home IoT system vulnerabilities. Existing smart home policy enforcement methods, designed for individual IoT admin platforms, operate under the assumption of having a global view and control over all IoT devices in a smart home. This assumption, however, is untenable in a multi-admin environment, where an admin may only have partial device access or a partial view. In this work, we thoroughly analyze these challenges in a multi-admin setting and propose the first cross-admin policy enforcement solution. Our solution can parse complex policies into deployable sub-policies for each admin, and create virtual device instances and virtual automation rules to interconnect various admins. We test our solution on a real-world testbed involving 12 IoT devices and three popular Matter-enabled IoT platforms. Our results show that our solution can enforce cross-admin policies with a 100% success rate and a very small delay.

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