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Hybrid Chaotic Salp Swarm with Crossover Algorithm for Underground Wireless Sensor Networks

computers, Materials & Continua 2022年第8期72卷 2963-2980页

作者： Mariem Ayedi Walaa H.ElAshmawi Esraa Eldesouky First Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia MEDIATRON Lab. SUP’COMCarthage UniversityTunis2083Tunisia Department of Computer Science Faculty of Computers and InformaticsSuez Canal UniversityIsmailia41522Egypt Faculty of Computer Science Misr International UniversityCairoEgypt

Resource management in Underground Wireless Sensor Networks(UWSNs)is one of the pillars to extend the network *** intriguing design goal for such networks is to achieve balanced energy and spectral resource *** paper focuses on optimizing the resource efficiency in UWSNs where underground relay nodes amplify and forward sensed data,received from the buried source nodes through a lossy soil medium,to the aboveground base station.A new algorithm called the Hybrid Chaotic Salp Swarm and Crossover(HCSSC)algorithm is proposed to obtain the optimal source and relay transmission powers to maximize the network resource *** proposed algorithm improves the standard Salp Swarm Algorithm(SSA)by considering a chaotic map to initialize the population along with performing the crossover technique in the position updates of *** experimental results,the HCSSC algorithm proves its outstanding superiority to the standard SSA for resource efficiency ***,the network’s lifetime is ***,the proposed algorithm achieves an improvement performance of 23.6%and 20.4%for the resource efficiency and average remaining relay battery per transmission,***,simulation results demonstrate that the HCSSC algorithm proves its efficacy in the case of both equal and different node battery capacities.

关键词： Underground wireless sensor networks resource efficiency chaotic theory crossover algorithm salp swarm algorithm

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HGFormer: Hierarchical Grouping Transformer for Domain Generalized Semantic Segmentation

HGFormer: Hierarchical Grouping Transformer for Domain Gener...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Jian Ding Nan Xue Gui-Song Xia Bernt Schiele Dengxin Dai NERCMS School of Computer Science Wuhan University China State Key Lab. LIESMARS Wuhan University China Max Planck Institute for Informatics Saarland Informatics Campus Germany

Current semantic segmentation models have achieved great success under the independent and identically distributed (i.i.d.) condition. However, in real-world applications, test data might come from a different domain than training data. Therefore, it is important to improve model robustness against domain differences. This work studies semantic segmentation under the domain generalization setting, where a model is trained only on the source domain and tested on the unseen target domain. Existing works show that Vision Transformers are more robust than CNNs and show that this is related to the visual grouping property of self-attention. In this work, we propose a novel hierarchical grouping transformer (HGFormer) to explicitly group pixels to form part-level masks and then whole-level masks. The masks at different scales aim to segment out both parts and a whole of classes. HGFormer combines mask classification results at both scales for class lab.l prediction. We assemble multiple interesting cross-domain settings by using seven public semantic segmentation datasets. Experiments show that HGFormer yields more robust semantic segmentation results than per-pixel classification methods and flat-grouping transformers, and outperforms previous methods significantly. Code will be availab.e at https://***/dingjianswl0l/HGFormer.

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HGFormer: Hierarchical Grouping Transformer for Domain Generalized Semantic Segmentation

arXiv

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arXiv 2023年

作者： Ding, Jian Xue, Nan Xia, Gui-Song Schiele, Bernt Dai, Dengxin NERCMS School of Computer Science Wuhan University China State Key Lab. LIESMARS Wuhan University China Max Planck Institute for Informatics Saarland Informatics Campus Germany

关键词： Semantic Segmentation

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Data-Driven Examination of the Impact Energy Efficiency has on Demand Response Capabilities in Institutional Buildings

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Journal of Engineering for Sustainable Buildings and Cities 2022年第2期3卷 024501页

作者： Andrews, Abigail Roth, Jonathan Jain, Rishee K. Mathieu, Johanna L. Urban Informatics Lab. Department of Civil and Environmental Engineering Stanford University 473 Via Ortega Stanford 94305 CA United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor 48109 MI United States

Growing concerns over climate change and grid reliability have led to widespread adoption of energy efficiency (EE) and demand response (DR) programs at utilities. Despite such adoption, numerous questions exist regarding the interactions between EE and DR, including whether EE diminishes a building’s DR potential. In this brief, we empirically examine the impact a building’s EE level (quantified by traditional EE benchmarking metrics) has on its DR capabilities (quantified by a building’s normalized load shed) for 194 K-12 institutional school buildings in California, USA. We found inconclusive statistical evidence that a building’s EE level has an impact on its DR load shed capabilities. We provide initial evidence countering concerns that EE diminishes DR potential and thus pave the path for future work than can further support synergistic EE and DR strategies which can enhance demand-side management programs. Copyright © 2022 by ASME.

关键词： benchmarking demand response demand-side management energy efficiency

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Joint Self-Supervised Image-Volume Representation Learning with Intra-inter Contrastive Clustering 37

Joint Self-Supervised Image-Volume Representation Learning w...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Nguyen, Duy M.H. Nguyen, Hoang Mai, Truong T.N. Cao, Tri Nguyen, Binh T. Ho, Nhat Swoboda, Paul Albarqouni, Shadi Xie, Pengtao Sonntag, Daniel Department of Computer Science University of Stuttgart Germany AISIA Lab. University of Science - VNU HCM Viet Nam Department of Multimedia Engineering Dongguk University Korea Republic of Department of Statistics and Data Sciences University of Texas Austin United States Max Planck Institute for Informatics Germany Helmholtz AI Helmholtz Munich Germany Clinic for Diagnostic and Interventional Radiology University of Bonn Germany Department of Electrical and Computer Engineering University of California San Diego United States Department of Computer Science Oldenburg University Germany German Research Center for Artificial Intelligence Germany

ISBN: (纸本)9781577358800

Collecting large-scale medical datasets with fully annotated samples for training of deep networks is prohibitively expensive, especially for 3D volume data. Recent breakthroughs in self-supervised learning (SSL) offer the ability to overcome the lack of lab.led training samples by learning feature representations from unlab.led data. However, most current SSL techniques in the medical field have been designed for either 2D images or 3D volumes. In practice, this restricts the capability to fully leverage unlab.led data from numerous sources, which may include both 2D and 3D data. Additionally, the use of these pre-trained networks is constrained to downstream tasks with compatible data dimensions. In this paper, we propose a novel framework for unsupervised joint learning on 2D and 3D data modalities. Given a set of 2D images or 2D slices extracted from 3D volumes, we construct an SSL task based on a 2D contrastive clustering problem for distinct classes. The 3D volumes are exploited by computing vectored embedding at each slice and then assembling a holistic feature through deformable self-attention mechanisms in Transformer, allowing incorporating long-range dependencies between slices inside 3D volumes. These holistic features are further utilized to define a novel 3D clustering agreement-based SSL task and masking embedding prediction inspired by pre-trained language models. Experiments on downstream tasks, such as 3D brain segmentation, lung nodule detection, 3D heart structures segmentation, and abnormal chest X-ray detection, demonstrate the effectiveness of our joint 2D and 3D SSL approach. We improve plain 2D Deep-ClusterV2 and SwAV by a significant margin and also surpass various modern 2D and 3D SSL approaches. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Embeddings

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Weaving Indigeneity and Culture into the Fabric of HCI Futures

Weaving Indigeneity and Culture into the Fabric of HCI Futur...

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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Farao, Jaydon Pillai, Ajit G. Mthoko, Hafeni Samuel, Marly Muudeni Elmimouni, Houda Lazem, Shaimaa HCI Lab. The University of Cape Town Western Cape Cape Town South Africa Affective Interactions Lab. The University of Sydney Sydney NSW Australia Independent Institute of Education Cape Town South Africa School of Simulation and Visualisation Glasgow School of Art Glasgow United Kingdom Department of Computer Science University of Manitoba Winnipeg MB Canada Informatics Research Institute City of Scientific Research and Technology Applications New Borg El-Arab Alexandria Egypt

ISBN: (纸本)9798400713958

Interconnectedness and relationality have become integral to technology development and innovation, which has led to indigenous and cultural philosophies and approaches becoming important in developing effective and inclusive design practices. This workshop challenges the dominant paradigms in Human-computer Interaction (HCI) by exploring the potential of indigeneity and culture to inform design methodologies. We invite participants to reflect on how such approaches can move HCI research toward more inclusive, culturally diverse, and mutually beneficial synergies. Through constructive conflict and co-creation, we aim to illuminate the potential of indigenous and cultural approaches in HCI practice, inspiring vital shifts towards decolonial and pluralistic design. We welcome HCI researchers and practitioners and indigenous scholars and experts interested in disrupting traditional methods and embracing indigenous and cultural lenses to join this critical conversation. © 2025 Copyright held by the owner/author(s).

关键词： community connectedness futuring indigenous practices

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eShadow+: Mixed Reality Storytelling Inspired by Traditional Shadow Theatre 46

eShadow+: Mixed Reality Storytelling Inspired by Traditional...

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46th IEEE Annual computers, Software, and Applications Conference, COMPSAC 2022

作者： Moumoutzis, Nektarios Christoulakis, Marios Xanthaki, Chara Maragoudakis, Yiannis Christodoulakis, Stavros Paneva-Marinova, Desislava Pavlova, Lilia Institute of Mathematics and Informatics Bulgarian Academy of Sciences Sofia Bulgaria School of Electrical & Computer Eng. Technical University of Crete Lab. of Distr. Multimedia Inf. Systems & Applications Chania Greece Computer Science Teacher Directorate of Secondary Education Chania Greece Academy of Sciences Laboratory of Telematics Bulgarian Sofia Bulgaria

ISBN: (纸本)9781665488105

eShadow is a digital storytelling platform inspired by traditional Shadow Theatre. It enables the creation of digital stories within a project-based approach that may start from scenario development and include the creation of digital puppets and sceneries, the set-up and recording of story scenes and the final assembly of a digital story. This paper presents how eShadow can be enhanced to solve the problem of creating mixed reality installations to offer rich learning experiences in informal learning settings. This enhanced version is eShadow+ and it is evaluated via two installations which are described and compared. The evaluation results demonstrate the effectiveness of the approach thus offering new learning opportunities that are aligned with current trends in the use of mixed reality technologies. © 2022 IEEE.

关键词： Theaters

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Deep Learning-Driven Resource Allocation for MEC-Enabled UAV Collision Avoidance System

Deep Learning-Driven Resource Allocation for MEC-Enabled UAV...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Khadidja Zairi Bouziane Brik Younes Guellouma Hadda Cherroun LIM Lab. Amar Telidji University Laghouat Algeria Computer Science Department College of Computing and Informatics Sharjah University Sharjah UAE DRIVE Laboratory EA1859 University of Burgundy 49 Rue Mademoiselle Bourgeois Nevers France

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

The Internet of Unmanned Aerial Vehicles (UAVs) envisions critical services like Collision detection and Avoidance among Vehicles (CAVs). These services are typically implemented at the Multi-access Edge Computing (MEC) to enable ultra-low latency communication, ensuring real-time reactions to prevent collisions. To ensure network coverage and optimal connection of UAVs to the nearest MEC host, the CAV must be deployed across all MEC hosts. However, this may impose additional demands on these hots’ availab.e computational and memory resources. We introduce an Artificial Intelligence empowered framework to optimize virtualized resource allocation to the MEC hosts. The framework harnesses the capabilities of Deep Learning (DL) for twofold pivotal objectives: (i) Forecasting UAV Density: the framework predicts the UAV density that each MEC host must accommodate. This anticipatory insight guides resource allocation, adapting it to the anticipated demand dynamics. (ii) Precision in Virtual Resource Allotment: DL is further instrumental in calibrating the precise quantum of virtual resources requisite for the collision detection application to function optimally. This approach ensures the collision avoidance system’s peak efficiency without unduly taxing MEC host computational capacities. Validation of the proposed framework entails empirical analysis. The experimental outcomes underscore the precision of the prediction model and corroborate the resource allocation framework’s efficacy.

关键词： Wireless communication Computational modeling computer architecture Predictive models Autonomous aerial vehicles Resource management Collision avoidance

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Mitigating the Massive Access Problem in the Internet of Things 1

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2nd International Symposium on Security in computer and Information sciences, EuroCybersec 2021

作者： Gelenbe, Erol Nakıp, Mert Marek, Dariusz Czachorski, Tadeusz Institute of Theoretical and Applied Informatics Polish Academy of Sciences Gliwice44-100 Poland Yaşar University Bornova İzmir Turkey CNRS Lab. I3S Université Côte d’Azur Nice France CNRS Lab. Abraham de Moivre London United Kingdom Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice44-100 Poland

ISBN: (数字)9783031093579

ISBN: (纸本)9783031093562

The traffic from the large number of IoT devices connected to the IoT is a source of congestion known as the Massive Access Problem (MAP), that results in packet losses, delays and missed deadlines for real-time data. This paper reviews the literature on MAP and summarizes recent results on two approaches that have been designed to mitigate MAP. One approach is based on randomizing the packet arrival instants to IoT gateways within a given time interval that is chosen so that packet arrivals do not exceed their deadlines, but also so that they do not constitute bulk arrivals. The second approach is a novel traffic shaping policy named the Quasi-Deterministic-Transmission-Policy (QDTP) which has been proved to drastically reduce queue formation at the receiving gateway by delaying packet departures from the IoT devices in a judicious manner. Both analytical and experimental results are summarized, that describe the benefits of these techniques. © 2022, The Author(s).

关键词： Internet of things

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DRDM: A Disentangled Representations Diffusion Model for Synthesizing Realistic Person Images

arXiv

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arXiv 2024年

作者： Huang, Enbo Zhang, Yuan Huang, Faliang Zhang, Guangyu Liu, Yang Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning China Zhidayuan AI Lab. Nanning China College of Mathematics and Informatics South China Agricultural University Guangzhou China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China

Person image synthesis with controllab.e body poses and appearances is an essential task owing to the practical needs in the context of virtual try-on, image editing and video production. However, existing methods face significant challenges with details missing, limbs distortion and the garment style deviation. To address these issues, we propose a Disentangled Representations Diffusion Model (DRDM) to generate photo-realistic images from source portraits in specific desired poses and appearances. First, a pose encoder is responsible for encoding pose features into a high-dimensional space to guide the generation of person images. Second, a body-part subspace decoupling block (BSDB) disentangles features from the different body parts of a source figure and feeds them to the various layers of the noise prediction block, thereby supplying the network with rich disentangled features for generating a realistic target image. Moreover, during inference, we develop a parsing map-based disentangled classifier-free guided sampling method, which amplifies the conditional signals of texture and pose. Extensive experimental results on the Deepfashion dataset demonstrate the effectiveness of our approach in achieving pose transfer and appearance control. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal encoding

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