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Mix of Experts Language Model for Named Entity Recognition

Mix of Experts Language Model for Named Entity Recognition

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international conference on Communications, Information System and computer Engineering (CISCE)

作者： Xinwei Chen Kun Li Tianyou Song Jiangjian Guo Department of Electronical and Computer Engineering University of Illinois at Urbana Champaign Urbana IL USA Department of Computer Science University of Illinois at Urbana Champaign Urbana IL USA Department of Computer Science Columbia University New York USA Department of Computer Science and Engineering University of California San Diego La Jolla US

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

Named Entity Recognition (NER) is an essential steppingstone in the field of natural language processing. Although promising performance has been achieved by various distantly supervised models, we argue that distant supervision inevitably introduces incomplete and noisy annotations, which may mislead the model training process. To address this issue, we propose a robust NER model named BOND-MoE based on Mixture of Experts (MoE). Instead of relying on a single model for NER prediction, multiple models are trained and ensembled under the Expectation-Maximization (EM) framework, so that noisy supervision can be dramatically alleviated. In addition, we introduce a fair assignment module to balance the document-model assignment process. Extensive experiments on real-world datasets show that the proposed method achieves state-of-the-art performance compared with other distantly supervised NER.

关键词： Training Annotations Computational modeling Named entity recognition Predictive models Noise measurement Information systems

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Frequency Analysis for Measuring Tremor with VR Nose-Finger Test Based on Optical Device

Frequency Analysis for Measuring Tremor with VR Nose-Finger ...

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international Symposium on Soft Computing and Intelligent systems (SCIS)

作者： Azuma Shiraiwa Kouki Nagamune Masafumi Kubota Hideaki Matsuo Graduate School of Engineering University of Fukui Fukui Japan Graduate School of Engineering University of Hyogo Hyogo Japan Faculty of Health Sciences Institute of Medical Pharmaceutical and Health Sciences Kanazawa University Kanazawa Japan Division of Physical Therapy and Rehabilitation Medicine University of Fukui Hospital Fukui Japan

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

Ataxia is caused by brain damage such as stroke or head trauma, and the muscles become unable to work in a coordinated manner even though there is no paralysis or loss of consciousness. A symptom of ataxia is tremor, which is an involuntary, rhythmic, vibratory movement. Such tremors affect daily movements and work, leading to a decrease in quality of life (QOL). Effective rehabilitation and identification of symptoms and disorders are important for improving such symptoms. However, in the Nose-Finger Test, which is one of the tests performed by physical therapists, there are concerns that the inter-rater reliability and clinical services will decrease because the evaluation is subjective. In addition, virtual reality (VR) equipment has developed in recent years and has become more common in our lives. Accordingly, VR rehabilitation is being developed. Therefore, in this study, we used the VR Nose-Finger Test, analyzed the frequency at the time of measurement to extract features depending on the presence of symptoms are present, and performed discrimination using machine learning to evaluate the effectiveness. As a result, differences depending on the presence of symptoms were observed from the frequency, and the effectiveness of machine learning judgment was demonstrated.

关键词： Performance evaluation Time-frequency analysis Solid modeling Machine learning Virtual reality Feature extraction Time measurement Frequency measurement Paralysis Reliability

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Optimal Scheduling in a Quantum Switch: Capacity and Throughput Optimality 25

Optimal Scheduling in a Quantum Switch: Capacity and Through...

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Abstracts of the 2025 ACM SIGMETRICS international conference on measurement and modeling of computer systems

作者： Sanidhay Bhambay Thirupathaiah Vasantam Neil Walton Durham University Business School Durham University Durham United Kingdom Department of Computer Science Durham University Durham United Kingdom

ISBN: (纸本)9798400715938

With a growing number of quantum networks in operation, there is a pressing need for performance analysis of quantum switching technologies. A quantum switch establishes, distributes, and maintains entanglements across a network. In contrast to a classical switching fabric, a quantum switch is a two-sided queueing network. The switch generates Link-Level Entanglements (LLEs) across links that connect users to the switch, which are then fused to process the network's entanglement requests. First, we characterize the capacity region that is defined as the set of entanglement request rates for which there exists a scheduling policy stabilizing the system. We then show that a sequence of Max-Weight policies that we propose achieve throughput optimality in the asymptotic sense. Our proof techniques analyse a two-time scale separation phenomenon at the fluid scale for a general switch topology. This allows us to demonstrate that the optimal fluid dynamics are given by a scheduling algorithm that solves a certain average reward Markov Decision Process.

关键词： Markov decision process.

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Colorectal Polyp Detection and Localization in Colonoscopy Images Based on YOLOv8

Colorectal Polyp Detection and Localization in Colonoscopy I...

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international conference on computer and Information Technology (ICCIT)

作者： Mainuddin Hasan Jahirul Islam Muhammed Rakibul Hasan Md. Khaliluzzaman Department of Computer Science and Engineering International Islamic University Chittagong (IIUC) Chittagong Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Our work focuses on determining the effectiveness of the YOLOv8 model in identifying colon polyps using the Kvasir dataset. Our research illustrates the YOLOv8 model’s exceptional performance throughout several epochs (50, 75, and 100), attaining elevated accuracy, recall, and mean Average accuracy (mAP) metrics. The model achieved a maximum mAP@0.5 of 99.3%, demonstrating its remarkable proficiency in polyp identification with an accuracy rate of 99.51%. The model achieved an F1-score of 98.8%, indicating an equitable balance between accuracy and recall. Comprehensive investigation demonstrated that augmenting training epochs improved detection accuracy, with optimal results noted between 50 to 75 epochs. The confusion matrix confirmed the extent to which the model could tell apart the different types of polyps thus placing YOLOv8 as a reliable tool in medical image analysis. This data implies that, depending on the final analysis, the YOLOv8 model can boost the quality and effectiveness of the first steps aimed at locating the polyp suggestion of the use of automated medical computer-aided systems.

关键词： Training measurement Deep learning Analytical models Accuracy Computational modeling Colonic polyps Robustness Medical diagnostic imaging Tuning

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A New Energy Battery Lightweight Network Based on YOLOv8

A New Energy Battery Lightweight Network Based on YOLOv8

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Frontiers Technology of Information and computer (ICFTIC), IEEE international conference on

作者： Dong Li Jiahui Zhang Inner Mongolia University of Technology Hohhot China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

With the rapid development of the new energy battery industry and the increasing demand for intelligent management systems, higher requirements for efficient and low-consumption detection technology have been put forward. Aiming at the problems of high complexity and high computational requirements of existing models, we propose a lightweight neural network model based on deep learning. Specifically, we replace the backbone network in the traditional model with StarNet as the base network, and replace the Bottleneck structure in C2f with its unique StarBlock, and optimize the detection header to accelerate the model training and improve the computational efficiency. The experimental results show that this improved strategy has achieved remarkable results. Improved YOLOv8n Lightweight Model mAP@0.5 Improved by 0.6%, mAP@0.5:0.95 Improved by 0.5%, reaching 81.2%. While maintaining the detection accuracy, the number of parameters has been reduced by 54.46%, the computational complexity has been reduced by 44.44%, the model volume has been reduced by 58.73%, and more importantly, the detection frame rate has been increased by 21.5 FPS. This allows the improved model to show great utility in the task of detecting defects in battery appearance in a real factory environment.

关键词： Training measurement Solid modeling Accuracy Computational modeling Neural networks Object detection Production facilities Batteries Computational complexity

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Analysis of correlations between electric arc furnace model coefficients

Analysis of correlations between electric arc furnace model ...

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international conference on Environment and Electrical Engineering (EEEIC)

作者： Maciej Klimas Dariusz Grabowski Department of Electrical and Computer Engineering Silesian University of Technology Gliwice Poland

Electric arc furnaces (EAFs) are the largest loads in power systems and are characterized by a significant negative influence on power quality. The modeling of EAFs provides the knowledge necessary for the proper design of power systems and power quality improvement systems. This paper introduces a new EAF model based on the Random Differential Equation resulting from an extension of the power balance arc model with stochastic processes. The proposed solution considers correlations between the coefficients of the equation, resulting in improved accuracy and simplification of the model. The developed solution is based on real measurement data originating from the melting stage of a large industrial EAF.

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From Static to Dynamic: A Deeper, Faster, and Adaptive Language modeling Approach

From Static to Dynamic: A Deeper, Faster, and Adaptive Langu...

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international joint conference on Neural Networks (IJCNN)

作者： Jiajia Li Qiwei Li Ping Wang School of Information Management Wuhan University Wuhan China School of Computer Science Wuhan University Wuhan China Center for the Studies of Information Resources School of Information Management Wuhan University Wuhan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Transformer-empowered Pre-trained language models (PrLM) have achieved great success in sumless natural language processing tasks. Meanwhile, keeping seeking better performance leads to deeper and deeper models, which is surely suboptimal by taking the same deep model for various input samples with different learnable complexities, let alone the unnecessity of too deep model for ‘easy’ input. Therefore in this work, we explore a dynamic use of Transformer layers. In detail, we propose a novel Transformer-the-Adaptive including an estimator module that enables the model to automatically learn and evaluate the complexity of every input samples, so that the model can adaptively use the most economic numbers of layers for inference. The proposed Transformer is implemented in the current state-of-the-art PrLM, ALBERT, giving a deeper, faster and adaptive model, ALBERTa. We train four-size optimized ALBERTa models including base, large, xlarge, and xxlarge and carry out experiments on three typical tasks MRC, NLU, and NER. Experiment results show that our proposed ALBERTa model optimized by Transformer-the-Adaptive has better performance and efficiency than the original ALBERT model, which fully reflects the effectiveness of Transformer-the-Adaptive.

关键词： Economics Adaptation models Adaptive systems Biological system modeling Neural networks Transformers Natural language processing

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Topological Representations of Heterogeneous Learning Dynamics of Recurrent Spiking Neural Networks

Topological Representations of Heterogeneous Learning Dynami...

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international joint conference on Neural Networks (IJCNN)

作者： Biswadeep Chakraborty Saibal Mukhopadhyay Dept. of Electrical and Computer Engineering Georgia Institute of Technology Atlanta USA

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Spiking Neural Networks (SNNs) have become an essential paradigm in neuroscience and artificial intelligence, providing brain-inspired computation. Recent advances in literature have studied the network representations of deep neural networks. However, there has been little work that studies representations learned by SNNs, especially using unsupervised local learning methods like spike-timing dependent plasticity (STDP). Recent work by [1] has introduced a novel method to compare topological mappings of learned representations called Representation Topology Divergence (RTD). Though useful, this method is engineered particularly for feedforward deep neural networks and cannot be used for recurrent networks like Recurrent SNNs (RSNNs). This paper introduces a novel methodology to use RTD to measure the difference between distributed representations of RSNN models with different learning methods. We propose a novel reformulation of RSNNs using feedforward autoencoder networks with skip connections to help us compute the RTD for recurrent networks. Thus, we investigate the learning capabilities of RSNN trained using STDP and the role of heterogeneity in the synaptic dynamics in learning such representations. We demonstrate that heterogeneous STDP in RSNNs yield distinct representations than their homogeneous and surrogate gradient-based supervised learning counterparts. Our results provide insights into the potential of heterogeneous SNN models, aiding the development of more efficient and biologically plausible hybrid artificial intelligence systems.

关键词： Training Accuracy Neuroscience Biological system modeling Computational modeling Supervised learning Reservoir computing

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Predicting F1-Scores of Classifiers in Network Intrusion Detection systems

Predicting F1-Scores of Classifiers in Network Intrusion Det...

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international conference on computer Communications and Networks (ICCCN)

作者： Priscila Silva Gaspard Baye Alexandre Broggi Nathaniel D. Bastian Gokhan Kul Lance Fiondella Electrical and Computer Engineering University of Massachusetts Dartmouth Dartmouth MA USA Computer and Information Science University of Massachusetts Dartmouth Dartmouth MA USA Army Cyber Institute United States Military Academy NY USA

ISBN: (数字)9798350384611

ISBN: (纸本)9798350348439

With the evolution of the Internet of Things, network intrusion detection systems (NIDS) are vital for protecting networks by monitoring and analyzing network traffic to detect potential cyber threats. Deep neural networks (DNNs) are widely used in NIDS for their accurate classification and response capabilities against threats. However, there is a lack of detailed discussion in the literature about evaluating DNN performance in real-time scenarios for monitoring and assurance of NIDS. This paper fills this gap by applying multiple linear regression models to predict the F1-score of a DNN attack classifier. The predictive models are evaluated using a pre-trained DNN on a NIDS benchmark dataset, where three different distance metrics computed between real-time instances and known attack patterns stored in historical data are considered as model covariates. Our findings show that the multiple linear regression model with interaction between covariates confidently forecasts the F1-score for future periods, demonstrating its ability to anticipate future observations with an empirical coverage of 94.4%.

关键词： measurement Computational modeling Linear regression Network intrusion detection Artificial neural networks Telecommunication traffic Predictive models

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Depth Reconstruction with Neural Signed Distance Fields in Structured Light systems

Depth Reconstruction with Neural Signed Distance Fields in S...

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international conference on 3D Imaging, modeling, Processing, Visualization and Transmission (3DIMPVT)

作者： Rukun Qiao Hiroshi Kawasaki Hongbin Zha Peking University China Kyushu University Japan

ISBN: (数字)9798350362459

ISBN: (纸本)9798350362466

We introduce a novel depth estimation technique for multi-frame structured light setups using neural implicit representations of 3D space. Our approach employs a neural signed distance field (SDF), trained through self-supervised differentiable rendering. Unlike passive vision, where joint estimation of radiance and geometry fields is necessary, we capitalize on known radiance fields from projected patterns in structured light systems. This enables isolated optimization of the geometry field, ensuring convergence and network efficacy with fixed device positioning. To enhance geometric fidelity, we incorporate an additional color loss based on object surfaces during training. Real-world experiments demonstrate our method’s superiority in geometric performance for few-shot scenarios, while achieving comparable results with increased pattern availability.

关键词： Training Performance evaluation Geometry Three-dimensional displays Estimation Color Rendering (computer graphics)

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