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Chain based Leader Selection using Neural Network in Wireless Sensor Networks protocols 9

Chain Based Leader Selection using Neural Network in Wireles...

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9th International Japan-Africa conference on Electronics, Communications, and Computations, JAC-ECC 2021

作者： El-Sayed, Hamdy H. Refaay, Shereen K. Ali, Samia A. El-Melegy, Moumen T. Sohag University Faculty of Computers and Artificial Intelligence Dept. of Computer Network Sohag Egypt Assiut University Faculty of Engineering Dept. of Electrical Engineering Assiut Egypt

ISBN: (纸本)9781665482929

In wireless sensor networks (WSNs), selecting a chain leader is a critical issue. In this paper, we present a novel method for selecting chain leaders in a chain-based routing protocols that utilizes a Neural Network (NN). Our proposed method is applicable to any chain-based routing protocol, such as PEGASIS (Power-Efficient Gathering in Sensor Information Systems) [6], CBERP (Cluster based Energy Efficient Routing Protocol) [15], CCM (Chain-Cluster based Mixed Routing Protocol) [14], CCBRP (Chain-Chain based Routing Protocol) [18], and others. To validate our claim that our method can be applied to any chain-based routing protocol, we checked it on two of the most well-known protocols, PEGASIS (the original chain-based routing protocol) and CCBRP. It is well recognized that energy consumption is a critical issue for all WSNs. Our proposed methodology uses the Neural Network as a tool to select chain leaders based on the residual energy of each network member node. the simulation results show that the use of our proposed method improved the performance of both PEGASIS and CCBRP in terms of consumed energy and network lifetime. © 2021 IEEE.

关键词： Routing protocols

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Achieving Efficient and Accurate Segmentation for Autonomous Driving

Achieving Efficient and Accurate Segmentation for Autonomous...

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International Symposium on Environmentally Conscious Design and Inverse Manufacturing (EcoDesign)

作者： Ningkang Ouyang Jie Dong Yi Han ChenYu Zhao School of Information Engineering North China University of Water Resources and Electric Power Zhengzhou China Department of Computer Science and Information Anyang Institute of Technology Engineering Anyang China School of Artificial Intelligence Zhengzhou Software Vocational and Technical College Zhengzhou China

ISBN: (数字)9798350363043

ISBN: (纸本)9798350363050

Autonomous driving technology, as a mainstream trend in today’s technological development, holds significant commercial value. Semantic segmentation, a core technology in this field, faces challenges with current mainstream models due to their complex structures, large numbers of parameters, and the need for high-performance hardware. these issues hinder their application in mobile devices. therefore, this paper proposes a road scene semantic segmentation algorithm based on dynamic asymmetric convolution and capsule networks, named Asymmetric Capsule Convolutional Networks (ACCN). Specifically, the algorithm uses dynamic asymmetric convolution to construct a baseline network that combines dynamic convolution and capsule dynamic routing algorithms to enhance the model’s representational capabilities. Additionally, a dispersed attention module is built to strengthen the dependency between features to achieve more adaptable network weights. Experiments demonstrate that ACCN achieves a good balance between segmentation accuracy and detection speed. On the CamVid and Cityscapes test datasets, the proposed method in this paper achieved an average Intersection over Union (mIoU) of $\mathbf{8 0. 8\%}$ at 55.2 fps, showcasing better trade-offs in segmentation accuracy and efficiency. the experimental results indicate the effectiveness of our method.

关键词： Accuracy Convolution Heuristic algorithms Semantic segmentation Roads Routing Hardware Mobile handsets Vehicle dynamics Autonomous vehicles

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Enhancing Practical Learning in Undergraduate Chemical engineering Courses via Integration of Commercial Process Modelling software

Enhancing Practical Learning in Undergraduate Chemical Engin...

引用

9th International conference on Interactive Collaborative and Blended Learning, ICBL 2020

作者： LaRue, Ryan J. Monaco, Isabella Latulippe, David R. Department of Chemical Engineering McMaster University HamiltonONL8S 4L7 Canada

ISBN: (纸本)9783030672089

Integrating commercial software packages into undergraduate engineering courses is seen as a beneficial pedagogical approach for students in two ways. First, it facilitates an active learning environment;second, it gives students access to modern technical tools. Here, we present the key outcomes from the incorporation of software packages in two chemical engineering courses: a commercial hydraulic modelling software (PIPE-FLO;Engineered software) was used in a Fluid Mechanics course, and a freely-available water treatment design software (WAVE;DuPont) was used in a Separations course. For each software package, a set of self-guided tutorials were created with step-by-step instructions (including screenshots/diagrams) and both closed- and open-ended practice problems that were designed to improve the learning outcomes. Also, a set of supplementary workshops were given to demonstrate the practicality of the software. this approach was expected to promote a greater understanding of course material by creating a low-risk environment where the students can explore and expand their knowledge. Over the past five years of integrating PIPE-FLO into the Fluid Mechanics course, the responses from the students have been overwhelmingly positive. Our approach has led to the successful internalization of course content, as evidenced by accreditation metrics. We anticipate that continually refining these course materials—especially with regards to the WAVE software—will see students develop a greater understanding of the course content. © 2021, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： software packages

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Deep Web Selection based on Entity Association 9th

Deep Web Selection Based on Entity Association

引用

9th International conference on Computer engineering and Networks, CENet2019

作者： Deng, Song Luo, Wen Xu, Xueke School of Software & Internet of Things Engineering Jiangxi University of Finance and Economics Nanchang China

ISBN: (纸本)9789811537523

Each subject has a vast amount of deep web resources nowadays. It becomes tremendously hard to retrieve the required integrated information from all the related deep web resources for a subject, which drives out the technology on how to select the suitable web resources. In the medical field, the associations among entities are very extensive, as it can improve our overall level of health when all the related association information can be integrated and used. To improve the entities and their associations integration efficiency, we provide a data resource selection method based on the entities and their associations. the entity and association abstract matrix used in the method is built on the weighted entity scores and their association information in the entity and association diagram. Also, based on the entity and association query intention, it provides a new way to calculate the data resource association. After a large amount of tests and experiments on the data sets in the medical field, the new method is shown to provide a better precision and recall, and it can greatly support the study of information integration in the medical field. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Matrix algebra

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On the mechanical properties of functionally graded hybrid metal foams

On the mechanical properties of functionally graded hybrid m...

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9th International conference on Advanced Materials and Structures, AMS 2022

作者： Szovák, Benedek Károly, Dóra Kemény, Alexandra Kemény, Dávid Miklós Kádár, Csilla Department of Materials Science and Engineering Faculty of Mechanical Engineering Budapest University of Technology and Economics Muegyetem rkp. 3. BudapestH-1111 Hungary MTA-BME Lendület Composite Metal Foams Research Group Muegyetem rkp. 3. BudapestH-1111 Hungary

Radial functionally graded foam-filled tubes were manufactured with a highly reproducible and cost-effective in-situ process. Aluminum alloy tubes were filled with differently arranged lightweight expanded clay aggregate particles and common road salt, and then infiltrated with molten aluminum. After leaching out the salt particles, the specimens were heat-treated, and quasi-static uniaxial compression tests were applied on them. the results showed that the compressive behaviour of the radial functionally graded foam-filled tubes was mainly determined by the characteristics of the tube, the arrangement of the particles in the foam part played only minor role. the difference in mechanical properties caused by the particle arrangement fells into standard deviation, while the aluminum separator raised the absolute value of the absorbed energy by 17% and the specific value by 4%, the absolute value of the compressive offset stress by 28% and the specific value by 16%, and the absolute value of the plateau stress by 25% and the specific value by 12%, approximately. © 2022 Elsevier Ltd. All rights reserved.

关键词： Compression testing

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ALTRec: Adversarial Learning for Autoencoder-based Tail Recommendation

ALTRec: Adversarial Learning for Autoencoder-based Tail Reco...

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International conference on Data Science and Advanced Analytics (DSAA)

作者： Jixiong Liu Dugang Liu Weike Pan Zhong Ming College of Computer Science and Software Engineering Shenzhen University

ISBN: (纸本)9781665473316

Autoencoder-based methods have achieved significant performance on item recommendation. However, they may not perform well on tail items due to the ignorance of the items’ popularity bias. As a response, in this paper, we focus on tail items and propose a novel adversarial learning method for tail recommendation (ALTRec). In our ALTRec, the generator (i.e., AutoRec) not only reconstructs the input well, but also minimizes the (any two-user) similarity difference between the input stage and the output stage to keep users’ interaction relationships unchanged. And the discriminator maps the inputs and outputs of the generator to a same semantic space for scoring the similarity and maximizes the similarity difference as the target, and will identify some unsatisfactory predictions, especially on tail items. In order to preserve the similarity, the generator will pay more attention to the tail items compared with the previous autoencoder-based methods. An ablation study validates the effectiveness of preserving the two-user similarity, as well as the adversarial learning strategy in our ALTRec. Extensive experiments on three real-world datasets show that our ALTRec significantly boosts the performance on tail items compared with several state-of-the-art methods.

关键词： Semantics Tail Data science Generators Adversarial machine learning

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Z-NetMF: A Biased Embedding Method based on Matrix Factorization 9th

Z-NetMF: A Biased Embedding Method Based on Matrix Factoriza...

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9th International conference on Communications, Signal Processing, and Systems, CSPS 2020

作者： Sun, Yuchen Wan, Liangtian Sun, Lu Wang, Xianpeng Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Dalian116620 China Department of Communication Engineering Institute of Information Science Technology Dalian Maritime University Dalian116026 China State Key Laboratory of Marine Resource Utilization in South China Sea and School of Information and Communication Engineering Hainan University Haikou570228 China

ISBN: (纸本)9789811584107

Network embedding represents the graph in low dimensions, improving the processing of big scale tasks. As node2vec can only be modeled as a tensor, and NetMF cannot be generalized to a biased form directly. In this paper, we draw Z-Laplacian, a framework that describes the dynamic process on the graph, into NetMF, so that NetMF can be extends to a biased form. the biased version of NetMF is named as Z-NetMF. We analyze the biased random walk in the view of graph signal processing and prove the effectiveness of Z-NetMF in the node classification task. the result shows that Z-NetMF outperforms the comparison methods, NetMF and node2vec. © 2021, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Network embeddings

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Defining Academic engineering Education Roles within the United States 9

Defining Academic Engineering Education Roles within the Uni...

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9th Research in engineering Education Symposium and 32nd Australasian Association for engineering Education conference: engineering Education Research Capability Development, REES AAEE 2021

作者： Bodnar, Cheryl A. McCave, Erin J. Smith-Orr, Courtney Strong, Alexandra Coso Faber, Courtney Lee, Walter Rowan University United States University of Tennessee Knoxville United States University of North Carolina Charlotte United States Florida International University United States Virginia Tech United States

ISBN: (纸本)9781713862604

CONTEXT engineering education is an interdisciplinary research field where scholars are commonly embedded within the context they study. engineering Education Scholars (EES), individuals who define themselves by having expertise associated with both engineering education research and practice, inhabit an array of academic positions, depending on their priorities, interests, and desired impact. these positions include, but are not limited to, traditional tenure-track faculty positions, professional teaching or research positions, and positions within teaching and learning centers or other centers. EES also work in diverse institutional contexts, including engineering disciplinary departments, first-year programs, and engineering education departments, which further vary their roles. PURPOSE OR GOAL the purpose of this preliminary research study is to better understand the roles and responsibilities of early-career EES. this knowledge will enable PhD programs to better prepare engineering education graduates to more intentionally seek positions, which is especially important given the growing number of engineering education PhD programs. We address our purpose by exploring the following research question: How can we describe the diversity of academic or faculty roles early-career EES undertake? APPROACH OR MEthODOLOGY/MEthODS We implemented an explanatory sequential mixed-methods study starting with a survey (n=59) to better understand the strategic actions of United States-based early-career EES. We used a clustering technique to identify clusters of participants based on these actions (e.g., teaching focused priorities, research goals). We subsequently recruited 14 survey participants, representing each of the main clusters, to participate in semi-structured interviews. through the interviews, we sought to gain a more nuanced understanding of each participant's actions in the contexts of their roles and responsibilities. We analyzed each interview transcript to deve

关键词： engineering education

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9th EAI International conference on Wireless Mobile Communication and Healthcare, MobiHealth 2020

9th EAI International Conference on Wireless Mobile Communic...

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9th EAI International conference on Wireless Mobile Communication and Healthcare, MobiHealth 2020

ISBN: (纸本)9783030705688

the proceedings contain 23 papers. the special focus in this conference is on Wireless Mobile Communication and Healthcare. the topics include: Design of a Mobile-based Neurological Assessment Tool for Aging Populations;improving Patient throughput by Streamlining the Surgical Care-Pathway Process;connect - Blockchain and Self-Sovereign Identity Empowered Contact Tracing Platform;expanding eVision’s Granularity of Influenza Forecasting;explainable Deep Learning for Medical Time Series Data;the Effects of Masking in Melanoma Image Classification with CNNs Towards International Standards for Image Preprocessing;Robust and Markerfree in vitro Axon Segmentation with CNNs;Using Bayesian Optimization to Effectively Tune Random Forest and XGBoost Hyperparameters for Early Alzheimer’s Disease Diagnosis;a Proposal of Clinical Decision Support System Using Ensemble Learning for Coronary Artery Disease Diagnosis;patient-Independent Schizophrenia Relapse Prediction Using Mobile Sensor based Daily Behavioral Rhythm Changes;Deep-Learning-based Feature Encoding of Clinical Parameters for Patient Specific CTA Dose Optimization;COVID-19 Patient Outcome Prediction Using Selected Features from Emergency Department Data and Feed-Forward Neural Networks;Validation of Omron Wearable Blood Pressure Monitor HeartGuideTM in Free-Living Environments;artificial Empathy for Clinical Companion Robots with Privacy-By-Design;understanding E-Mental Health for People with Depression: An Evaluation Study;Evaluating Memory and Cognition via a Wearable EEG System: A Preliminary Study;Towards Mobile-based Preprocessing Pipeline for Electroencephalography (EEG) Analyses: the Case of Tinnitus;forecasting Health and Wellbeing for Shift Workers Using Job-Role based Deep Neural Network;a Deep Learning Model for Exercise-based Rehabilitation Using Multi-channel Time-Series Data from a Single Wearable Sensor;bayesian Inference Federated Learning for Heart Rate Prediction.

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TGL_sta: Low-resource Textual Graph Learning with Semantic and Topological Awareness via LLMs 39

TGL<sub>sta</sub>: Low-resource Textual Graph Learning with ...

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39th Annual AAAI conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Qin Li, Xiaowei Liu, Ziqi Fan, Xiaochen Chen, Xiaojun Pan, Shirui College of Computer Science and Software Engineering Shenzhen University China Institute for Electronics and Information Technology in Tianjin Tsinghua University China School of Information and Communication Technology Griffith University Australia

ISBN: (纸本)157735897X

Textual Graphs (TGs) present a graph-based representation of textual data and find wide applications in real-world scenarios, such as citation networks, knowledge graphs, and social networks. While the traditional “pre-train, fine-tune” framework effectively addresses tasks requiring abundant labeled data, it falls short in scenarios with limited resource or zero-shot learning capabilities, particularly in low-resource textual graph node classification. Additionally, prevalent approaches that convert text nodes into shallow or manually engineered features fail to capture the rich semantic nuances within the text. the conventional methods often neglect the fusion of semantic and topological information, resulting in suboptimal model learning. To overcome these challenges, we proposed a novel method of low-resource textual graph node classification based on large language models, i.e., Textual graph learning with semantic and topological awareness (TGLsta), which comprehensively explores the semantic information, near neighborhood information, and the topology information in textual graphs, where these components are the most important information source contained in textual graphs. Graph prompt tuning for both zero- and few-shot textual graph node classification is further introduced. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Zero-shot learning

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