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Optimizing Message Delivery in Opportunistic Networks with Replication-Based Forwarding

Optimizing Message Delivery in Opportunistic Networks with R...

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2024 International conference on Engineering and Computing, ICECT 2024

作者： Ashfaq, Muhammad Ahmad, Tanveer Anwar, Asim Irshad, Azeem Benitez, Ian B. Murtaza, Marryam University of Engineering and Technology Department of Electronics Engineering Taxila Pakistan University of Cyprus and CYENS Centre of Excellence Department of Computer Science Nicosia Cyprus The University of Lahore Department of Technology Lahore Pakistan International Islamic University Islamabad Department of CS&SE Islamabad Pakistan College of Engineering FEU Institute of Technology Electrical Engineering Department Manila1015 Philippines COMSATS University Wah Cantt Department of Computer Science Wah cantt Pakistan

ISBN: (纸本)9798350349719

In opportunistic networks, no end-To-end path is available from source to destination due to frequent movement of nodes with high speed. In such type of networks, transmission takes places between nodes during a contact event. These types of networks, follow store-carry and forward mechanism to forward messages from source to destination, Intermediate node stores messages into buffer and carries these messages until it meets another node. Already existing flooding protocols like epidemic may congest the network due to excessive flooding of the messages over the network. Replication based routing protocols introduces in which messages are replicating according to quota value. The replication based protocols have some limitations like delay which degrades the performance of network. Our proposed technique overcome the limitations of replication based routing protocols. Proposed technique provides replication based forwarding with optimal buffer management to increase the delivery ratio and minimize the delay. Extensive simulation of proposed technique is done in ONE simulator with different scenarios and comparing result of proposed scheme with already existing schemes such as epidemic, Rep-nodes and Spray & Wait. Result shows that our scheme has outperform as compare to already existing schemes in terms of delivery ratio and delay. © 2024 IEEE.

关键词： Routing protocols

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Target-Speaker Voice Activity Detection Via Sequence-to-Sequence Prediction 48

Target-Speaker Voice Activity Detection Via Sequence-to-Sequ...

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48th IEEE International conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Cheng, Ming Wang, Weiqing Zhang, Yucong Qin, Xiaoyi Li, Ming Wuhan University School of Computer Science Wuhan China Duke Kunshan University Data Science Research Center Kunshan China

ISBN: (纸本)9781728163277

Target-speaker voice activity detection is currently a promising approach for speaker diarization in complex acoustic environments. This paper presents a novel Sequence-to-Sequence Target-Speaker Voice Activity Detection (Seq2Seq-TSVAD) method that can efficiently address the joint modeling of large-scale speakers and predict high-resolution voice activities. Experimental results show that larger speaker capacity and higher output resolution can significantly reduce the diarization error rate (DER), which achieves the new state-of-the-art performance of 4.55% on the VoxConverse test set and 10.77% on Track 1 of the DIHARD-III evaluation set under the widely-used evaluation metrics. © 2023 IEEE.

关键词： Sequence-to-Sequence Transformers Speaker Diarization Target-Speaker Voice Activity Detection

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Real-Time Thyroid Nodule Precise MIoT Leveraging Quad-Node Raspberry Pi Clusters and NVIDIA Jetson Nano for Edge AI 3

Real-Time Thyroid Nodule Precise MIoT Leveraging Quad-Node R...

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3rd International conference on Computing, Communication, Perception and Quantum Technology, CCPQT 2024

作者： Liu, Xian-Xian Tan, Lijian Yang, Jie Department of Computer and Information Science University of Macau China College of Intelligent Manufacturing Chongqing Industry and Trade Polytechnic Chongqing China College of Artificial Intelligence Chongqing Industry and Trade Polytechnic Chongqing China

ISBN: (纸本)9798331528386

This study introduces the thyroid nodule segmentation grid search based local patch learning (GS-LPL) network as an effective IoT solution for real-time, precise thyroid cancer segmentation. Utilizing the Turing PI and NVIDIA Jetson Nano, we developed a quadruple node distributed computing cluster for real-time cancer segmentation through parallel and distributed computing. GS-LPL features a lightweight neural network, optimized for mini-supercomputers, enabling efficient model training and deployment. The NVIDIA Jetson Nano supports real-time high-definition video processing, ideal for low-powered intelligent video analytics like Network Video Recorders (NVR). Our model is also deployable on mobile devices with limited resources. Experimental results validate our model’s fast, efficient, and accurate performance in thyroid nodule cancer segmentation. © 2024 IEEE.

关键词： Cluster computing

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Simple Contrastive Learning with Knowledge Graphs for Story Generation

Simple Contrastive Learning with Knowledge Graphs for Story ...

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2025 IEEE International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhu, Yu Pan, Rong School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (纸本)9798350368741

Story generation stands as a crucial, yet formidable task, necessitating a profound grasp of subtle, often unspoken knowledge, along with context-specific cues to craft compelling narratives. The core challenges involve effectively harnessing this implicit knowledge and augmenting narrative diversity. Addressing these hurdles, this paper introduces SimCoS - an acronym for Simple Contrastive learning with knowledge graphs for Story generation. SimCoS significantly bolsters language models' performance in story generation by sharpening their ability to perceive and discern input nuances, aided by a specialized story knowledge graph. Moreover, SimCoS debuts a novel methodology for evaluating 'negative instances' using subgraphs, which enables it to calculate logical distances between inputs, thereby enhancing differentiation among diverse stories. Through both automatic and manual assessments, SimCoS has been shown to outperform leading counterparts in two story generation tasks, marking improvements of 13.4% and 4.5%, respectively. This advancement not only yields narratives of superior quality and logical coherence but also elevates their diversity. © 2025 IEEE.

关键词： contrastive learning knowledge graph story generation

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An Explainable Intelligent Approach for Cardiovascular Disease Diagnosis from high-Dimensional Laboratory Test Results 13

An Explainable Intelligent Approach for Cardiovascular Disea...

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13th International conference on Information Technology in Medicine and Education, ITME 2023

作者： Chen, Hao Zhou, Yunfan Li, Ying Sun, Xuxue Shen, Shanshan Zeng, Hao Polytechnic Institute Zhejiang University Hangzhou China College of Computer Science Zhejiang University Hangzhou China College of Media Engineering Communication University of Zhejiang Hangzhou China Zhejiang University Zhejiang Hospital Hangzhou China

ISBN: (纸本)9798350319156

Cardiovascular diseases (CVD) constitute a group of chronic ailments that can have sudden onset, emphasizing the crucial need for their prediction and early prevention. The current clinical diagnosis of CVD often requires comprehensive examinations, including laboratory and imaging tests, symptom checks, and physical examinations. These procedures can be time-consuming, labor-intensive, and expensive for patients. Additionally, the utilization of accessible tabular laboratory test results, which are typically high-dimensional and imbalanced, presents substantial challenges for CVD diagnosis. To address these issues, we propose an explainable intelligent approach for CVD diagnosis by analyzing high-dimensional and imbalanced laboratory test results. Firstly, a conditional tabular generative adversarial network is introduced to address the issue of data imbalance. Furthermore, the proposed approach utilizes a one-dimensional convolutional neural network with self-attention mechanism to improve the accuracy and efficiency of CVD diagnosis. Moreover, by extracting the attention vectors, the importance of different features is analyzed to enhance the interpretability of the model. As a result, the proposed approach outperforms other methods by demonstrating improved prediction performance, achieving an accuracy rate of 88.89%. Furthermore, the most influential features that contribute to discovering the CVD diagnosis pattern are identified as risk factors, offering enhanced support for the accurate prevention of CVD. © 2023 IEEE.

关键词： Diagnosis

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An Adaptive Varying-Parameter Projection Neural Network for Redundant Manipulator with Unknown Physical Parameters 17th

An Adaptive Varying-Parameter Projection Neural Network for...

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17th International conference on Intelligent Robotics and Applications, ICIRA 2024

作者： Huang, Pan Hou, Che Chen, Wenjie Zheng, Lunan College of Information Science and Engineering Northeastern University Shenyang110819 China State Key Laboratory of High-end Heavy-Load Robots Midea Group Foshan528300 China Midea Corporate Research Center Foshan528311 China School of Automation Science and Engineering South China University of Technology Guangzhou510640 China

ISBN: (纸本)9789819607761

Quadratic programming methods have been widely used to solve the redundancy problem of control manipulators due to their ability to optimize performance indicators under physical constraints. However, conventional quadratic programming methods require known parameters. In practical work environments, the physical parameters of the manipulator can be uncertain. Therefore, an adaptive control method that can synchronize parameter identification and network control is necessary. We propose an adaptive varying parameter projection neural network method that begins from the perspective of joint velocity space. This method decouples the unknown parameters and nonlinear parts that require identification in the Jacobian matrix of the manipulator. For the first time, a projection network with varying parameters is introduced for redundant decomposition, greatly improving the accuracy of trajectory tracking. Additionally, we design an iterative identification equation based on neural dynamics and introduce position feedback, making Jacobian matrix identification more accurate. The proposed method can solve the problem of redundant resolution of robotic arms under actual physical constraints and unknown physical parameters and can improve performance indicators. Theoretical analysis and simulation results demonstrate the feasibility and performance of the proposed method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Robotic arms

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Learning Asynchronous Common and Individual Functional Brain Network for AD Diagnosis 26th

Learning Asynchronous Common and Individual Functional Brain...

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26th International conference on Medical Image Computing and computer-Assisted Intervention (MICCAI)

作者： Tang, Xiang Zhang, Xiaocai Liu, Mengting Zhang, Jianjia Sun Yat Sen Univ Sch Biomed Engn Shenzhen Campus Shenzhen 518107 Peoples R China ASTAR Inst High Performance Comp Singapore 138632 Singapore

ISBN: (纸本)9783031439926;9783031439933

Construction and analysis of functional brain network (FBN) with rs-fMRI is a promising method to diagnose functional brain diseases. Traditional methods usually construct FBNs at the individual level for feature extraction and classification. There are several issues with these approaches. Firstly, due to the unpredictable interferences of noises and artifacts in rs-fMRI, these individual-level FBNs have large variability, leading to instability and unsatisfactory diagnosis accuracy. Secondly, the construction and analysis of FBNs are conducted in two successive steps without negotiation with or joint alignment for the target task. In this case, the two steps may not cooperate well. To address these issues, we propose to learn common and individual FBNs adaptively within the Transformer framework. The common FBN is shared, and it would regularize the FBN construction as prior knowledge, alleviating the variability and enabling the network to focus on these disease-specific individual functional connectivities (FCs). Both the common and individual FBNs are built by specially designed modules, whose parameters are jointly optimized with the rest of the network for FBN analysis in an end-to-end manner, improving the flexibility and discriminability of the model. Another limitation of the current methods is that the FCs are only measured with synchronous rs-fMRI signals of brain regions and ignore their possible asynchronous functional interactions. To better capture the actual FCs, the rs-fMRI signals are divided into short segments to enable modeling cross-spatiotemporal interactions. The superior performance of the proposed method is consistently demonstrated in early AD diagnosis tasks on ADNI2 and ADNI3 data sets.

关键词： Functional brain network Alzheimer's disease rs-fMRI computer-aided diagnosis Transformer

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Feline Feelings Unleashed: Harnessing Deep Learning Through Photos for Cat Pain Detection 4

Feline Feelings Unleashed: Harnessing Deep Learning Through ...

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4th IEEE International conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Namboonlue, Chutimon Virankabutra, Pirada Sa-Ing, Vera Srinakharinwirot University Faculty of Science Department of Computer Science Bangkok Thailand Chulalongkorn University Faculty of Veterinary Science Department of Surgery Bangkok Thailand

ISBN: (纸本)9798350360721

Effective pain detection in cats is challenging. Thus, we classified feline pain using a dataset of 57 images per category, labeled 'pain' or 'no pain' by Thai veterinarians using the performance of EfficientNetB7, a pre-trained convolutional neural network. The images were preprocessed through various configurations of EfficientNetB7 in differing batch sizes and learning rates. ImageNet weights were used as the initial training parameters. The models were evaluated based on accuracy, precision, and recall. The most effective model was the SGD optimizer with a learning rate of 0.001 and a batch size of 100. The model achieved 79% accuracy, 74% precision, and 90% recall. These findings demonstrated the potential of deep learning for non-verbal pain detection in veterinary, especially with the high recall rate to identify animals in distress. The research results showed the possibility of integrating such AI models into veterinary practice, enhancing animal welfare. © 2024 IEEE.

关键词： Convolutional neural networks

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4D Gaussian Splatting with Scale-aware Residual Field and Adaptive Optimization for Real-time Rendering of Temporally Complex Dynamic Scenes 24

4D Gaussian Splatting with Scale-aware Residual Field and Ad...

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32nd ACM International conference on Multimedia, MM 2024

作者： Yan, Jinbo Peng, Rui Tang, Luyang Wang, Ronggang School of Electronic and Computer Engineering Peking University Shenzhen China Pengcheng Laboratory Shenzhen China

ISBN: (纸本)9798400706868

Reconstructing dynamic scenes from video sequences is a highly promising task in the multimedia domain. While previous methods have made progress, they often struggle with slow rendering and managing temporal complexities such as significant motion and object appearance/disappearance. In this paper, we propose SaRO-GS as a novel dynamic scene representation capable of achieving real-time rendering while effectively handling temporal complexities in dynamic scenes. To address the issue of slow rendering speed, we adopt a Gaussian primitive-based representation and optimize the Gaussians in 4D space, which facilitates real-time rendering with the assistance of 3D Gaussian Splatting. Additionally, to handle temporally complex dynamic scenes, we introduce a Scale-aware Residual Field. This field considers the size information of each Gaussian primitive while encoding its residual feature and aligns with the self-splitting behavior of Gaussian primitives. Furthermore, we propose an Adaptive Optimization Schedule, which assigns different optimization strategies to Gaussian primitives based on their distinct temporal properties, thereby expediting the reconstruction of dynamic regions. Through evaluations on monocular and multi-view datasets, our method has demonstrated state-of-the-art performance. © 2024 ACM.

关键词： Rendering (computer graphics)

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Proton-recoil spectrometer for fast neutron spectrum based on GEM gas detector 49

Proton-recoil spectrometer for fast neutron spectrum based o...

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49th EPS conference on Plasma Physics, EPS 2023

作者： Scholz, M. Dąbrowski, W. Wiącek, U. Jardin, A. Lach, B. Drozdowicz, K. Woźnicka, U. Kurowski, A. Kulińska, A. Mazon, D. KrakowPL-31-342 Poland AGH University of Science and Technology Faculty of Physics and Applied Computer Science Krakow Poland CEA IRFM Saint-Paul-lez-DuranceF-13108 France

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