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Information and Network Technologies (ICINT), International Conference on

作者： Yotam Ashkenazi Shlomi Dolev Sergey Frenkel Manish Kumar Sweta Kumari Ram Prasadh Narayanan Archit Somani Department of Computer Science Ben-Gurion University of the Negev Beersheba Israel The Institute of Informatics Problems Russian Academy of Science Russia Department of Computer Science New Jersey Institute of Technology New Jersey United States Department of Computer Science and Engineering Shiv Nadar University Delhi India Institute for Information and Communication Systems Science Walton Institute Ireland

ISBN: (数字)9798331536756

ISBN: (纸本)9798331536763

Broadcasting stands as a vital function within wireless sensor networks (WSNs), where nodes disseminate messages throughout the entire network, radiating in all possible directions. Our focus lies on the issue of three-dimensional broadcasting, involving n sensors/robots positioned uniformly across a three-dimensional space. We propose an exponentially fast hybrid collaborative broadcasting algorithm in three-dimensional space "growing ball" through successive propagation steps. We study the effect of various parameters such as the density of the growing ball (ρ), distance (d), the energy emitted by node (E emit ), and ϵ used by the collaborative broadcast. Our approach is based on the idea of dividing the 3D space into two halves that reduce mathematical intricacy. The number of nodes N k in the k th step of the growing ball is the factor of (1+ϵ) k implies the exponential fast broadcast.

关键词： Wireless sensor networks Three-dimensional displays Collaboration Broadcasting

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Toward Enhancing Cross-Lingual Domain Knowledge Sharing and Transferring for Multilingual Domain Adaptation in NMT

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 2125-2138页

作者： Zhengtao Yu Jinlei Xu Xiang Huang Zhongfen Deng Yonghua Wen Li Sun Yuxin Huang Hao Peng Philip S. Yu Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Province Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming China School of Cyber Science and Technology Beihang University Beijing China Department of Computer Science University of Illinois Chicago IL USA School of Control and Computer Engineering North China Electric Power University Beijing China

Multilingual Neural Machine Translation (NMT) excels in sharing knowledge across languages and transferring insights from high-resource languages to improve performance in low-resource languages. However, its performance lags in specific domains such as legal and medical. Previous works have focused on adding language-specific and domain-specific adapters to achieve domain adaptation. Although effective, these adapter-based methods only use domain data to train additional parameters, limiting the performance of multilingual NMT. In this paper, we propose CDSTX, a novel approach that achieves robust multilingual domain adaptation without relying on adapters, solely leveraging multilingual models. Specifically, we utilize the pretrained model XLM-R and frozen embeddings to preserve its robust multilingual capabilities and design a two-stage training strategy for domain adaptation. This includes separate training for the decoder and fine-tuning of the entire model, ensuring that the model effectively acquires domain knowledge and correctly represents domain-specific text. Moreover, to better utilize the domain information conveyed implicitly by the training data, we devise special domain tokens at the beginning of the source and target sentences called Source&Target Domain Tags. In addition, back translation is employed to enhance the cross-lingual transfer ability of our approach. Our proposed method is evaluated on two datasets across three translation tasks: Single-domain Multilingual NMT, Multi-domain bilingual NMT, and Multilingual Multi-domain NMT. Notably, in the single-domain multilingual NMT task, CDSTX significantly enhances zero-shot domain translation performance, achieving improvements of up to +30 BLEU points through the utilization of back-translation techniques. Even in the bilingual multi-domain NMT task where specific domain data for the target translation direction is unavailable, our method consistently outperforms all SOTA methods. Moreover, in the

关键词： Training Adaptation models Translation Limiting Law Large language models Training data Neural machine translation Multilingual Speech processing

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Coordinated control of distributed multi-agent systems using event-triggered predictive control strategy

Coordinated control of distributed multi-agent systems using...

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Fully Actuated System Theory and Applications (CFASTA), Conference on

作者： Lei Cao Chuanguo Chi Erhan Yumuk Clara-Mihaela Ionescu Guo-Ping Liu Department of Control Science and Engineering Harbin Institute of Technology Harbin P. R. China Department of Electromechanical Systems and Metal Engineering Dynamical Systems and Control Research Group Ghent University Ghent Belgium Department of Control and Automation Engineering Istanbul Technical University Istanbul Turkey Center for Control Science and Technology Southern University of Science and Technology Shenzhen P. R. China

In this paper, the coordinated control problem for a class of distributed networked multi-agent systems (NMASs) is studied. An event-triggered predictive coordinated control (ETPCC) scheme is proposed to compensate for the communication delay between controllers as well as to reduce the communication frequency. With the aim of reducing the frequency of data transfer among controllers and saving resources, a distributed event-triggered condition is designed to decide when to transfer information. Further, a cost function using predicted values is proposed to measure the cooperative performance among the agents, and the required control increment for each agent is obtained by minimizing the cost function. Simulation and experimental examples show that the proposed ETPCC strategy works effectively.

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MARViN: Mobile AR Dataset with Visual-Inertial Data

MARViN: Mobile AR Dataset with Visual-Inertial Data

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Changkun Liu Yukun Zhao Tristan Braud Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Division of Integrative Systems and Design The Hong Kong University of Science and Technology Hong Kong

ISBN: (数字)9798350374490

ISBN: (纸本)9798350374506

Accurate camera relocalisation is a fundamental technology for extended reality (XR), facilitating the seamless integration and persistence of digital content within the real world. Benchmark datasets that measure camera pose accuracy have driven progress in visual re-localisation research. Despite notable progress in this field, there is a limited availability of datasets incorporating Visual Inertial Odometry (VIO) data from typical mobile AR frameworks such as ARKit or ARCore. This paper presents a new dataset, MARViN, comprising diverse indoor and outdoor scenes captured using heterogeneous mobile consumer devices. The dataset includes camera images, ARCore or ARKit VIO data, and raw sensor data for several mobile devices, together with the corresponding ground-truth poses. MARViN allows us to demonstrate the capability of ARKit and ARCore to provide relative pose estimates that closely approximate ground truth within a short timeframe. Subsequently, we evaluate the performance of mobile VIO data in enhancing absolute pose estimations in both desktop simulation and user study. MARViN is available at https://***/XRIM-Lab/MarViN.

关键词： Performance evaluation Visualization Solid modeling Three-dimensional displays Pose estimation User interfaces Cameras

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Simultaneous Quantum Information and Power Transfer-Based Green QKD Receiver Architectures

Simultaneous Quantum Information and Power Transfer-Based Gr...

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International Communication systems and Networks and Workshops, COMSNETS

作者： Riyansh Chaturvedi Akhil Misra Sainath Bitragunta Ashuthosh Bhatia Kamlesh Tiwari Department of Electrical and Electronics Engineering BITS Pilani Pilani Rajasthan India Department of Computer Science and Information Systems BITS Pilani Pilani Rajasthan India

Considering the carbon footprint of rapidly evolving quantum systems and technologies, it is essential to develop energy efficient and sustainable next generation quantum communication systems. Simultaneous Lightwave Information and Power Transfer (SLIPT) enables the transfer of power and information from a transmitter to a receiver through light waves in the infrared (IR) and visible portions of the electro-magnetic spectrum. In this work, the authors propose QKD-featured, SLIPT-enabled (QuIPT) quantum communication hybrid receiver architectures. Specifically, the authors present simplified green QKD receiver architectures integrating free space QKD and SLIPT. The proposed system is useful for developing QKD-featured autonomous quantum Internet of Light Things (QIoLT). The simplified architecture comprises two key modules: the QKD-decryption and decoding module and the energy harvesting subsystem module. Finally, the authors present additional remarks on extending the architectures to include radio frequency based energy harvesting (RFbEH) modules for superior performance.

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A comparative analysis of MQTT and IoT application protocols 2

A comparative analysis of MQTT and IoT application protocols

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2nd IEEE International Conference on Electronic engineering, ICEEM 2021

作者： Al Enany, Marwa O Harb, Hany M. Attiya, Gamal Ministry of Higher Education Higher Institute of Computer Science and Information System Computer Science Department Egypt Al-Azhar University Faculty of Engineering Systems and Computers Department Cairo Egypt Menoufia University Faculty of Electronic Engineering Computer Science and Engineering Department Egypt

ISBN: (纸本)9781665418423

Choosing the appropriate IoT protocol for a certain application was and still a critical issue for IoT applications, as protocols vary in behavior in different applications and under different network status. IoT application protocols have a recent focus of attention especially those protocols for the low-powered IoT applications and constrained devices. A brief survey of IoT layers and applied protocols for each layer and their applications were presented, besides, a detailed survey of IoT protocols with a presentation of the main researches that have been done to evaluate their performance. As a use case for this comparative study, the MQTT protocol was chosen because of its simplicity and its publish /subscribe model to be evaluated and compared against other application protocols. © 2021 IEEE.

关键词： Surveys

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Quaternion Tensor Completion Via Qr Decomposition and Nuclear Norm Minimization

SSRN

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SSRN 2024年

作者： Sun, Jian Liu, Xin Zhang, Yang School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology China Macau Institute of Systems Engineering Faculty of Innovation Engineering Macau University of Science and Technology China Department of Mathematics University of Manitoba Winnipeg Canada

The task of tensor (matrix) completion has been widely used in the fields of computer vision and image processing, etc. To achieve the completion, the existing methods are mostly based on singular value decomposition of the real tensor and nuclear norm minimization. However, the real tensor completion methods cannot simultaneously maintain color channel correlation and evolution robustness of color video frames, they need high computational cost to handle the high-dimensional data. Hence they have some limitations in model generalization ability and computational efficiency. In this paper, a new completion method for the quaternion tensor (matrix) is explored via the QR decomposition and the definition of novel quaternion tensor norm, which can well balance the model generalization ability and efficiency, and the performance of the completion method has been substantially improved. Numerical experiments on color images and videos prove the effectiveness of our proposed method. © 2024, The Authors. All rights reserved.

关键词： Tensors

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Multi-VM-Workloads Learning-based Forecasting Model for Resilient Server Management in Industry Clouds

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Procedia computer science 2025年 260卷 923-929页

作者： Deepika Saxena Ashutosh Kumar Singh Division of Information Systems University of Aizu Aizuwakamatsu 965-0006 Fukushima Japan Department of Computer Science and Engineering Indian Institute of Information Technology Bhopal Bhopal 462003 Madhya Pradesh India

Forecasting server contention in large-scale cloud data center networks is crucial for efficient cloud resource management. The existing research primarily focuses on single-task machine learning methods for predicting resource-constrained VM failures, which often results in lower accuracy and higher computational costs. To overcome these challenges, this paper introduces a novel approach aimed at enhancing resilient server management in industry clouds. The proposed approach develops Multi-tasks-Learning-based Long Short-Term Memory for multiple VMs workload forecasting model named MVL-LSTM to simultaneously learn resource requirements of all VM hosted on a common server. It facilitates a single-step forecasting of workload resource requirements for all VM segments hosted on a server within a cluster. Experimental results using real-world Google Cluster VM datasets demonstrate that MVL-LSTM significantly improves forecasting accuracy while achieving faster execution and higher energy efficiency compared to existing single-task machine learning algorithms. Specifically, MVL-LSTM model achieves a 15% reduction in processing time and a 50% decrease in energy consumption compared with LSTM-based approach.

关键词： Cloud computing VM failure prediction MVL-LSTM Server management

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Passivity of nonlinear time-delay systems

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IFAC-PapersOnLine 2023年第2期56卷 8568-8573页

作者： Yu Kawano Saeed Ahmed Michele Cucuzzella Jacquelien M.A. Scherpen Graduate School of Advanced Science and Engineering Hiroshima University Higashi-hiroshima 739-8527 Japan Jan C. Willems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen 9747 AG Groningen The Netherlands Department of Electrical Computer and Biomedical Engineering University of Pavia 27100 Pavia Italy

This paper presents passivity-based control of nonlinear systems with retarded delays in the state. To this end, we first show that the standard passivity concept can naturally be generalized to time-delay systems, which readily implies that a feedback interconnection (with or without communication delays) of passive time-delay systems is also passive. Then, we propose a storage functional for passivity analysis and further use it for stability analysis of controlled-passive time-delay systems. In particular, invoking an invariance principle for retarded functional differential equations, we show that a passive time-delay system can always be stabilized by a static output feedback controller under a delayed version of the zero-state detectability assumption.

关键词： Passivity nonlinear systems time-delay systems retarded functional differential equations stabilizing control

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Image Caption Generation Related to Object Detection and Colour Recognition Using Transformer-Decoder

Image Caption Generation Related to Object Detection and Col...

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International Conference on Computing, Mathematics and engineering Technologies (iCoMET)

作者： Zainab Umair Kamangar Ghulam Mutjaba Shaikh Saif Hassan Nimra Mughal Umair Ayaz Kamangar Computer Science Department Sukkur IBA University Sukkur Pakistan CRAIB Computer Science Department Sukkur IBA University Sukkur Pakistan Computer Systems Engineering Department Sukkur IBA University Sukkur Pakistan

The dependence on digital images is increasing in different fields. i.e, education, business, medicine, or defense, as they are shifting towards the online paradigm. So, there is a dire need for computers and other similar machines to interpret information related to these images and help the users understand the meaning of it. This has been achieved with the help of automatic Image captioning using different prediction models, such as machine learning and deep learning models. However, the problem with the traditional models, especially machine learning models, is that they may not generate a caption that accurately represents that Image. Although deep learning methods are better for generating captions of an image, it is still an open research area that requires a lot of work. Therefore, a model proposed in this research uses transformers with the help of attention layers to encode and decode the image token. Finally, it generates the image caption by identifying the objects along with their colours. The fliker8k and Conceptual Captions datasets are used to train this model, which contains images and captions. The fliker8k contains 8,092 images, each with five captions, and Conceptual Captions contains more than 3 million images, each with one caption. The contribution of this presented work is that it can be utilized by different companies, which require the interpretation of diverse images automatically and the naming of the images to describe some scenario or descriptions related to the images. In the future, the accuracy can be increased by increasing the number of images and captions or incorporating different deep-learning techniques.

关键词： Deep learning Training Image color analysis Text recognition Computational modeling Companies Predictive models

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