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IEEE International Symposium on Quality Electronic Design

作者： Xiaotian Ma Jiaqi Tang Yu Bai Department of Computer Science California State University Fullerton Fullerton CA USA Computer Engineering Program California State University Fullerton Fullerton CA USA

As deep neural networks have continuously advanced computer version tasks, researchers from academia and industry focus on developing a powerful deep neural network model to process volumes of data. With the increasing size of DNN models, their inference process is computationally expensive and limits the employment of DNNs in real-time *** response, we present the proposed Locality-sensing Fast Neural Network (LFNN), a generalized framework for accelerating the querying videos process via locality sensing to reduce the cost of DNN in video evaluation by three times saving in inference time. The LFNN framework can automatically sense the similarity between two input frames via a defined locality from a given input video. The LFNN framework enables us to process the input videos within the specialized processing method that is far less computationally expensive than conventional DNN inference that conducts detection for each frame. Within the highlighted temporal locality information across frames, the Yolov5 algorithm can be accelerated by two to three times. Experimental results show that the proposed LFNN is easily implemented on the FPGA board with neglectable extra hardware costs.

关键词： Deep learning Costs Computational modeling Neural networks Real-time systems Hardware Software

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Brain Tumor Detection and Segmentation Using Deep Learning Models

Brain Tumor Detection and Segmentation Using Deep Learning M...

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Artificial Intelligence (WSAI), World Symposium on

作者： Moses Kazeem Abiodun Abidemi Emmanuel Adeniyi Joseph Bamidele Awotunde Halleluyah Aworinde George Tinuoye Agbotiname Lucky Imoize Chun-Ta Li Cheng-Chi Lee Department of Computer Science Landmark University Omu-Aran Kwara State Nigeria Department of Computer Science Bowen University Iwo Osun State Nigeria Department of Computer Science University of Ilorin Ilorin Kwara State Nigeria Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Nigeria Bachelor's Program of Artificial Intelligence and Information Security Fu Jen Catholic University New Taipei City Taiwan Department of Library and Information Science Fu Jen Catholic University New Taipei City Taiwan

ISBN: (数字)9798350374162

ISBN: (纸本)9798350374179

This study focuses on brain tumor detection and segmentation using Convolutional Neural Networks (CNN) with architectures of Fully Convolutional Net-work (FCN) and VGG16. The dataset imported for this study consists of brain MRI images, which were preprocessed to improve the overall image resolution. Data augmentation techniques were applied to heighten the variability and volume of the training data. For feature extraction, CNN models were utilized, specifically the VGG16 architectures. This model was trained on a preprocessed dataset to learn the important features associated with the presence of brain tumor. Image segmentation was performed to precisely outline and identify the tumor region within the brain images. FCN architecture was employed for this task, which allowed pixel-level classification of tumor regions. Finally, image classification was carried out to group brain images based on the absence or presence of a tumor. The trained models were evaluated using accuracy, precision, recall and F1score as the performance metrics. The results of the study demonstrated promising outcomes, achieving an accuracy score of 93% and a precision, recall and f1score ranging from 75% to 100% based on the specific type of tumor enlisted. This indicates that the developed CNN models, with the FCN and VGG16 architectures, effectively detect and segment brain tumors from MRI images. The findings of this study contribute to the field of medical image analysis and gives a foundation for further research and development in brain tumor diagnosis and treatment. The study recommends the use of more deep learning algorithms for the process of detecting brain tumors.

关键词： Deep learning Image segmentation Accuracy Magnetic resonance imaging Computational modeling Brain tumors Brain modeling Distance measurement Classification algorithms Convolutional neural networks

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A General Framework for Clustering and Distribution Matching with Bandit Feedback

arXiv

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arXiv 2024年

作者： Yavas, Recep Can Huang, Yuqi Tan, Vincent Y.F. Scarlett, Jonathan Descartes Program CNRS@CREATE 138602 Singapore Department of Information Systems and Analytics School of Computing National University of Singapore 119077 Singapore Department of Mathematics Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore Department of Computer Science Department of Mathematics Institute of Data Science National University of Singapore 119077 Singapore

We develop a general framework for clustering and distribution matching problems with bandit feedback. We consider a K-armed bandit model where some subset of K arms is partitioned into M groups. Within each group, the random variable associated to each arm follows the same distribution on a finite alphabet. At each time step, the decision maker pulls an arm and observes its outcome from the random variable associated to that arm. Subsequent arm pulls depend on the history of arm pulls and their outcomes. The decision maker has no knowledge of the distributions of the arms or the underlying partitions. The task is to devise an online algorithm to learn the underlying partition of arms with the least number of arm pulls on average and with an error probability not exceeding a pre-determined value δ. Several existing problems fall under our general framework, including finding M pairs of arms, odd arm identification, and N-ary clustering of K arms belong to our general framework. We derive a non-asymptotic lower bound on the average number of arm pulls for any online algorithm with an error probability not exceeding δ. Furthermore, we develop a computationally-efficient online algorithm based on the Track-and-Stop method and Frank–Wolfe algorithm, and show that the average number of arm pulls of our algorithm asymptotically matches that of the lower bound. Our refined analysis also uncovers a novel bound on the speed at which the average number of arm pulls of our algorithm converges to the fundamental limit as δ *** Codes 68T05 Copyright © 2024, The Authors. All rights reserved.

关键词： Clustering algorithms

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Percolation Nature of Threshold Switching: an Experimental Verification

Percolation Nature of Threshold Switching: an Experimental V...

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IEEE Conference on Nanotechnology

作者： Suman Devkota Brendan M Kuzior Victor G. Karpov Daniel G. Georgiev Frank Li Vamsi Borra Electrical and Computer Engineering program Youngstown State University Youngstown OH OH USA Department of Physics and Astronomy and Department of Electrical Engineering and Computer Science respectively University of Toledo Toledo OH USA

This paper details a process used to create an interconnect in a conducting systems, such as amorphous or polycrystalline semiconductors. An experimental verification on the plasticity that supports the percolation conduction mechanism is provided. The plasticity observed in the sample could be harnessed in the development of new electronic devices that require flexibility and adaptability, such as wearable electronics and bendable screens. Overall, TEM characterization, in combination with SAED analysis, revealed a highly oriented crystalline structure in the sample. In addition, the results of this study have implications for the design of new memory devices that are based on a percolation conduction mechanism, which could potentially lead to the development of more efficient and reliable non-volatile storage technologies.

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Improving Out-of-Distribution Generalization in Graphs via Hierarchical Semantic Environments

Improving Out-of-Distribution Generalization in Graphs via H...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yinhua Piao Sangseon Lee Yijingxiu Lu Sun Kim Department of Computer Science and Engineering Seoul National University Institute of Computer Technology Seoul National University AIGENDRUG Co. Ltd. Interdisciplinary Program in Artificial Intelligence Seoul National University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Out-of-distribution (OOD) generalization in the graph domain is challenging due to complex distribution shifts and a lack of environmental contexts. Recent methods attempt to enhance graph OOD generalization by generating flat environments. However, such flat environments come with inherent limitations to capture more complex data distributions. Considering the DrugOOD dataset, which contains diverse training environments (e.g., scaffold, size, etc.), flat contexts cannot sufficiently address its high heterogeneity. Thus, a new challenge is posed to generate more seman-tically enriched environments to enhance graph invariant learning for handling distribution shifts. In this paper, we propose a novel approach to generate hierarchical seman-tic environments for each graph. Firstly, given an input graph, we explicitly extract variant subgraphs from the in-put graph to generate proxy predictions on local environ-ments. Then, stochastic attention mechanisms are employed to re-extract the subgraphs for regenerating global environ-ments in a hierarchical manner. In addition, we introduce a new learning objective that guides our model to learn the diversity of environments within the same hierarchy while maintaining consistency across different hierarchies. This approach enables our model to consider the relationships between environments and facilitates robust graph invariant learning. Extensive experiments on real-world graph data have demonstrated the effectiveness of our framework. Par-ticularly, in the challenging dataset DrugOOD, our method achieves up to 1.29% and 2.83% improvement over the best baselines on IC50 and EC50 prediction tasks, respectively.

关键词： Training computer vision Attention mechanisms Computational modeling Semantics Stochastic processes Pattern recognition

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Neuronal Modeling Tool Using DynaSim 5

Neuronal Modeling Tool Using DynaSim

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5th IEEE Research and Applications of Photonics in Defense Conference, RAPID 2022

作者： Gieser, Anna Vitola, Sabina Enochson, Avery Wolf, Henry Striker, Ryan Cleveland, Jerika Braaten, Benjamin D. Ewert, Daniel Mitra, Dipankar Allen, Jeffery Allen, Monica North Dakota State University Department of Electrical and Computer Engineering Fargo United States Minnesota State University Bell Program Department of Integrated Engineering MankatoMN United States Program of Minnesota State University Department of Integrated Engineering at Iron Range Engineering MankatoMN United States University of Wisconsin - la Crosse Department of Computer Science and Computer Engineering La CrosseWI United States Munitions Directorate Air Force Research Laboratory Eglin Air Force Base FL32542 United States

ISBN: (数字)9781665402231

ISBN: (纸本)9781665402231

Human cognitive processes remain an area of strong interest and ongoing research. One tool to gain greater insight into this process is neuronal modeling. The following features are desirable in a neuronal modeling tool: a library of known parameters for different neurons and species, the ability to select the neuron and species of interest, the ability to quickly simulate neuronal behavior, and the ability to calculate metabolic requirements and efficiencies for various neuronal activity. Many of these can be found in software today, but not all elements can be found in one entity. The goal of this work is to develop a neuronal modeling tool that incorporates these features. © 2022 IEEE.

关键词： Neurons

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From Role-Play to Drama-Interaction: An LLM Solution

arXiv

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arXiv 2024年

作者： Wu, Weiqi Wu, Hongqiu Jiang, Lai Liu, Xingyuan Hong, Jiale Zhao, Hai Zhang, Min Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China Harbin Institute of Technology Shenzhen China

Drama is a form of storytelling inspired by human creativity, proceeding with a predefined storyline, carrying emotions and thoughts. This paper introduces LLM-based interactive drama, which endows traditional drama with an unprecedented immersion, where a person is allowed to walk into it and interact with the characters and scenes. We define this new artistic genre by 6 essential elements-plot, character, thought, diction, spectacle and interaction-and study the entire pipeline to forge a backbone drama LLM to drive the playing process, which is challenged by limited drama resources, uncontrollable narrative development, and complicated instruction following. We propose Narrative Chain to offer finer control over the narrative progression during interaction with players;Auto-Drama to synthesize drama scripts given arbitrary stories;Sparse Instruction Tuning to allow the model to follow sophisticated instructions. We manually craft 3 scripts, Detective Conan, Harry Potter, Romeo and Juliet, and design a 5-dimension principle to evaluate the drama LLM comprehensively. Copyright © 2024, The Authors. All rights reserved.

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The KIND Dataset: A Social Collaboration Approach for Nuanced Dialect Data Collection 18

The KIND Dataset: A Social Collaboration Approach for Nuance...

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18th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2024 - Student Research Workshop, SRW 2024

作者： Yamani, Asma Z. Alziyady, Raghad AlYami, Reem Albelali, Salma A. Abouhagar, Leina Almulhim, Jawharah Alsulami, Amjad Alfarraj, Motaz Al-Zaidy, Rabeah Department of Information and Computer Science King Fahd University of Petroleum & Minerals Saudi Arabia Center for Integrative Petroleum Research CIPR King Fahd University of Petroleum & Minerals Saudi Arabia Department of Electrical Engineering King Fahd University of Petroleum & Minerals Saudi Arabia SDAIA-KFUPM Joint Research Center for AI King Fahd University of Petroleum & Minerals Saudi Arabia Preparatory Year Program King Fahd University of Petroleum & Minerals Saudi Arabia Department of Computer Science Imam Abdulrahman Bin Faisal University Saudi Arabia Saudi Data & AI Authority Saudi Arabia

ISBN: (纸本)9798891760905

Nuanced dialects are a linguistic variant that pose several challenges for NLP models and techniques. One of the main challenges is the limited amount of datasets to enable extensive research and experimentation. We propose an approach for efficiently collecting nuanced dialectal datasets that are not only of high quality, but are versatile enough to be multipurpose as well. To test our approach we collect the KIND corpus, which is a collection of fine-grained Arabic dialect data. The data is short texts, and unlike many nuanced dialectal datasets, it is curated manually through social collaboration efforts as opposed to being crawled from social media. The collaborative approach is incentivized through educational gamification and competitions for which the community itself benefits from the open source dataset. Our approach aims to achieve: (1) coverage of dialects from under-represented groups and fine-grained dialectal varieties, (2) provide aligned parallel corpora for translation between Modern Standard Arabic (MSA) and multiple dialects to enable translation and comparison studies, (3) promote innovative approaches for nuanced dialect data collection. We explain the steps for the competition as well as the resulting datasets and the competing data collection systems. The KIND dataset is shared with the research community. © 2024 Association for Computational Linguistics.

关键词： Data acquisition

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A Comparison of Multi Class Support Vector Machine vs Deep Convolutional Neural Network for Brain Tumor Classification

A Comparison of Multi Class Support Vector Machine vs Deep C...

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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Nur Ryan Dwi Cahyo Christy Atika Sari Eko Hari Rachmawanto Cahaya Jatmoko Rabei Raad Ali Al-Jawry Mohamed Ayad Alkhafaji Department of Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Department Computer Science Northern Technical University Semarang Indonesia Department of Computer Engineering Technology Northern Technical University Mosul Iraq Department of Computer Sciece National University of Science and Technology DhiQar Iraq

Multi SVM has long been one of the popular methods in classification, while DCNN has recently gained significant attention in image processing and pattern recognition. This research evaluates the effectiveness of Multi Class Support Vector Machine (M-SVM) and Deep Convolutional Neural Network (DCNN) techniques in classifying brain tumors. A dataset of 2660 3D medical images with dimensions 227 x 227 x 3; including Glioma, Meningioma, and Pituitary tumors, has been partitioned into distinct sets for both training and testing purposes. DCNN approach achieves excellent accuracy in identifying tumor names, with a training accuracy of 97.8% and 100% success rate in 9 experiments. The Multi SVM method demonstrates relatively good accuracy, with training accuracies ranging from 70% to 90% based on different kernel functions. These findings provide valuable insights for selecting appropriate methods in brain tumor classification and encourage further exploration of hybrid Multi SVM-DCNN approaches to enhance accuracy and reliability.

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Convolutional Neural Network Using Res-Net For Organic and Anorganic Waste Classification

Convolutional Neural Network Using Res-Net For Organic and A...

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International Conference of computer science and Information Technology

作者： Abba Suganda Girsang Rezky Yunanda Muhammad Edo Syahputra Ezra Peranginangin Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350399080

Convolutional Neural network is state of the art of image recognition or image classification. However to build the robust model using CNN needs many parameters adjusted, and choosing the good combination hyperparameter which impacts taking much computation time. Genetic algorithm is one method metaheuristic which is robust for choosing the combinatorial possible hyperparameter. This model also uses ResNet-50 which is a pretrained model of convolutional neural network that consists 50 layers. By using a pretrained like ResNet-50, it will increase the performance model. CNN-ResNet with efficient genetic algorithm (EGA) to optimize the hyperparameter. The EGA algorithm utilizes transfer learning techniques in its algorithm so that the optimization process on CNN can achieve unified accuracy values quickly. The best performance model optimized using EGA outperformed the ResNet-50 model and the model optimized using GA and VLGA in classifying organic and inorganic materials. The accuracy value obtained from EGA is 97.53% with a loss of 0.08.

关键词： Image recognition Computational modeling Transfer learning Metaheuristics Inorganic materials Iron Classification algorithms

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