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6th International Conference on Communications, Information System and computer engineering, CISCE 2024

作者： Dang, Bo Zhao, Wenchao Li, Yufeng Ma, Danqing Yu, Qixuan Zhu, Elly Yijun San Francisco Bay University Computer Science FremontCA United States School of Software Engineering University of Science and Technology of China Anhui Hefei China School of Electronics and Computer Science University of Southampton Southampton United Kingdom Stevens Institute of Technology Computer Science HobokenNJ United States College of Computing Georgia Institute of Technology AtlantaGA United States

ISBN: (纸本)9798350353174

The prevalence of mobile technology offers unique opportunities for addressing healthcare challenges, especially for individuals with visual impairments. This paper explores the development and implementation of a deep learning-based mobile application designed to assist blind and visually impaired individuals in real-time pill identification. Utilizing the YOLO framework, the application aims to accurately recognize and differentiate between various pill types through real-time image processing on mobile devices. The system incorporates Text-to-Speech (TTS) to provide immediate auditory feedback, enhancing usability and independence for visually impaired users. Our study evaluates the application's effectiveness in terms of detection accuracy and user experience, highlighting its potential to improve medication management and safety among the visually impaired community. © 2024 IEEE.

关键词： Deep Learning Healthcare Mobile Application Pill Identification Visual Impairment YOLO Framework

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ScIAMC:Single-Cell Imputation via adaptive matrix completion 10

ScIAMC:Single-Cell Imputation via adaptive matrix completion

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Zhang, Shuai Chen, Xiang Peng, Li Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan Hunan411201 China Hunan Key Laboratory for Service computing and Novel Software Technology China

ISBN: (纸本)9798350312461

Single-cell sequencing techniques are often impacted by technical noise, leading to the generation of very sparse expression matrices. This technical noise is referred to as dropouts and poses as a major challenge for downstream analysis. In this study, we introduce scIAMC (single-cell imputation via adaptive parameter matrix completion), which is based on matrix completion theory to recover missing values in expression matrices. To expedite the algorithm's running time and avoid any parameter tuning on data, we formulated an optimization problem. Our approach led to an enhanced cell population identification and minimal errors, while also restoring biological landscapes that were damaged by these dropouts. © 2023 IEEE.

关键词： Cell proliferation

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Research on the Application of Improved Attention Mechanism in Image Classification and Object Detection

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2023年第4期50卷 1页

作者： Ding, Y.Y. Wang, L. School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China

In recent years, with the advancement of computer processing power and the rapid development of convolutional neural networks, attention models have been widely used in image classification and object detection, significantly improving the performance of networks. However, most existing methods focus on increasing the depth of networks, which inevitably leads to efficiency issues. To strike a balance between performance and complexity, this paper proposes an optimization based on Triplet Attention, introducing multi-scale attention fusion Triplet Attention and self-attention mechanisms. Firstly, the proposed model is integrated into YOLOv5s and tested on VOC2012, achieving a 3.9% increase in mean average precision (mAP). Then, the model is integrated into ResNet50 and tested on CIFAR-10 and CIFAR-100 datasets. The results show that the proposed model achieves a 1.79% improvement in Top-1 accuracy and a 0.13% improvement in Top-5 accuracy on CIFAR-10,a 3.11% improvement in Top-1 accuracy, and a 1.3% improvement in Top-5 accuracy on CIFAR-100. The experimental results demonstrate that the proposed model significantly enhances network performance. © (2023), (International Association of Engineers). All Rights Reserved.

关键词： Object detection

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Metaheuristic Algorithms for Proof Searching in HOL4 34

Metaheuristic Algorithms for Proof Searching in HOL4

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34th International Conference on software engineering and Knowledge engineering, SEKE 2022

作者： Nawaz, M. Saqib Nawaz, M. Zohaib Hasan, Osman Fournier-Viger, Philippe School of Computer Science and Software Engineering Shenzhen University China Department of Computer Science and IT University of Sargodha Sargodha Pakistan Islamabad Pakistan

ISBN: (纸本)1891706543

User guided proof development in interactive theorem proving is a manual and time consuming activity. For automating proof searching and optimization in a higher-order logic proof assistant, we provide two metaheuristic algorithms that are based on Fitness Dependent Optimizer (FDO) and Bat Algorithm (BA). In both metaheuristic algorithms, random proof sequences are first created from a population of frequently occurring proof steps that are discovered using pattern mining techniques. Created proof sequences are then evolved till their fitness matches the fitness of the original (or target) proof sequences. Experiments are performed to investigate the performance of the proposed algorithms on different HOL4 theories. Moreover, the proposed FDO and BA-based proof searching approaches are compared with Simulated Annealing (SA) and Genetic Algorithm (GA)based methods. Results show that BA performs best, followed by FDO and SA for proof finding and optimization in HOL4. © 2022 Knowledge Systems Institute Graduate school. All rights reserved.

关键词： Simulated annealing

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Collaborative Multi-Agent Proximal Policy Based Resource Allocation in Clustered IoV Networks 10

Collaborative Multi-Agent Proximal Policy Based Resource All...

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10th IEEE Smart World Congress, SWC 2024

作者： Raza, Arif Xiang, Junfan Khan, Salabat Shrivastava, Shivanshu Ashraf, M. Wasim Abbas Wang, Lu Shenzhen University College of Computer Science and Software Engineering Shenzhen China Shenzhen University College of Electronics and Information Engineering Shenzhen China Qilu Institute of Technology College of Computer and Information Engineering Jinan China Rajiv Gandhi Institute of Petroleum Technology Department of Electrical and Electronics Engineering Amethi India Hanjiang Normal University School of Mathematics and Computer Science Shiyan China

ISBN: (纸本)9798331520861

The Internet of Vehicles (IoV) necessitates efficient resource management to meet the growing demands for high data rates, low latency, and real-time communication in Intelligent Transportation Systems (ITS). This paper presents a novel multiagent reinforcement learning framework based on the Proximal Policy Optimization (PPO) algorithm for optimizing resource allocation in clusters of cells within IoV networks. The framework dynamically allocates transmission power and bandwidth across base stations, each acting as a collaborative agent. Extensive simulations demonstrate that the multi-agent PPO approach outperforms other reinforcement learning algorithms, including Soft Actor-Critic (SAC) and Advantage Actor-Critic (A2C), regarding throughput, power efficiency, dynamic bandwidth allocation, and overall network performance. © 2024 IEEE.

关键词： Bandwidth

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TH-SLP: Web Service Link Prediction Based on Topic-aware Heterogeneous Graph Neural Network

TH-SLP: Web Service Link Prediction Based on Topic-aware Het...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Peng, Qian Cao, Buqing Xie, Xiang Liu, Shanpeng Kang, Guosheng Liu, Jianxun Hunan University of Science and Technology Xiangtan School of Computer Science and Engineering Hunan Provincial Key Lab. for Services Computing and Novel Software Technology Hunan China

ISBN: (纸本)9798350304855

With the emergence of more and more Web services, finding suitable services becomes a difficult problem. Service link prediction is employed to disclose relationships among services, which facilitates the further development of service composition, selection, and recommendation. But the existing link prediction approaches simply utilize the structural features of the service network. In reality, the rich text content in service node description documents also carries latent but fine-grained semantics generated by multifaceted topic-aware factors, yet few efforts are committed to mining them. In this paper, we propose a Web service link prediction method based on a topic-aware heterogeneous graph neural network. Specifically, the method consists of two main layers, including the meta-path intra-decomposition and the meta-path inter-mergence. Meta-path intra-decomposition aims to mine the topic distribution of the meta-paths-based context while capturing fine-grained topic-aware semantics. Meta-path inter-mergence uniquely aggregates topic-aware factors according to the mined distribution and adopts a multifaceted attention mechanism to aggregate different meta-paths, enabling service nodes to generate multifaceted topic-aware embeddings that preserve not only the structure and but also the topic-aware semantics. In addition, a topic prior guidance regularization item is set up for quality assurance of multifaceted topic-aware embedding that depends on global knowledge of the unstructured text content in description documents. Experimental results on real datasets show that our proposed model outperforms other existing baselines methods in the link prediction task, successfully validating the effectiveness of our proposed method. © 2023 IEEE.

关键词： Semantics

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A Multistage Framework for Detection of Very Small Objects 23

A Multistage Framework for Detection of Very Small Objects

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6th International Conference on Machine Vision and Applications, ICMVA 2023

作者： Don, Duleep Rathgamage Aygun, Ramazan Karakaya, Mahmut School of Data Science and Analytics College of Computing and Software Engineering Kennesaw State University MariettaGA30060 United States Department of Computer Science College of Computing and Software Engineering Kennesaw State University MariettaGA30060 United States

ISBN: (纸本)9781450399531

Small object detection is one of the most challenging problems in computer vision. Algorithms based on state-of-the-art object detection methods such as R-CNN, SSD, FPN, and YOLO fail to detect objects of very small sizes. In this study, we propose a novel method to detect very small objects, smaller than 8×8 pixels, that appear in a complex background. The proposed method is a multistage framework consisting of an unsupervised algorithm and three separately trained supervised algorithms. The unsupervised algorithm extracts ROIs from a high-resolution image. Then the ROIs are upsampled using SRGAN, and the enhanced ROIs are detected by our two-stage cascade classifier based on two ResNet50 models. The maximum size of the images used for training the proposed framework is 32×32 pixels. The experiments are conducted using rescaled German Traffic Sign Recognition Benchmark dataset (GTSRB) and downsampled German Traffic Sign Detection Benchmark dataset (GTSDB). Unlike MS COCO and DOTA datasets, the resulting GTSDB turns out to be very challenging for any small object detection algorithm due to not only the size of objects of interest but the complex textures of the background as well. Our experimental results show that the proposed method detects small traffic signs with an average precision of 0.332 at the intersection over union of 0.3. © 2023 ACM.

关键词： Object detection

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An Improved Bi-LSTM EEG Emotion Recognition Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第3期7卷 623-639页

作者： Ma, Shuai Cui, Jianfeng Chen, Chin-Ling Xiao, Weidong Liu, Lijuan School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China College of Software Engineering Xiamen University of Technology Xiamen361024 China Department of Computer Science and Information Engineering Chaoyang University of Technology Taichung41349 Taiwan

EEG emotion signal feature extraction is computationally intensive and the classification accuracy of the model is not high. Therefore, it can seriously affect the overall performance of classification algorithms. In order to better apply deep learning methods to EEG emotion recognition classification. To address the above problems, this paper uses the Fast Fourier Transformation algorithm to extract richer and more complete multi-channel feature information in feature extraction. At the same time, for the problem of low classification accuracy, an improved Bi-LSTM classification model is proposed. Finally, the model proposed in this paper is verified by 32-channel EEG signal data. The experimental results show that the classification method proposed improves the accuracy of Arousal to 93.35% and Valence to 93.09%. Compared with other models, the improved Bi-LSTM model has an excellent performance in EEG emotion recognition. Meanwhile, this paper explores the brain emotion mechanism by the accuracy of the improved model for different brain region channels, showing that the brain regions with the highest emotional relevance are the parietal and frontal lobes in the brain. © 2022 Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Emotion Recognition

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Deep Learning Object Detection 7th

Deep Learning Object Detection

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7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Liu, Jingnian Huang, Weihong Xiao, Lijun Huo, Yingzi Xiong, Huixuan Li, Xiong Xiao, Weidong School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan411201 China Guangdong Financial High-Tech Zone "Blockchain +" Fintech Research Institute Foshan528253 China School of Computer Science and Engineering University of Electronic Science and Technology of China Xiangtan411201 China School of Software Engineering Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9783031281235

Object detection techniques are a major part of computer vision research, with large-scale applications in industrial, scientific and other scenarios. Technologies such as face detection, medical image detection, autonomous driving, and traffic detection have played a significant role in people’s lives. With the rapid development of deep learning, many application areas, such as image classification, text classification, machine translation, etc., have achieved breakthrough success in combination with deep learning. R-CNN brings object detection into the era of deep learning, and its advantage compared with traditional methods is that the former requires personnel to extract features manually, while the latter uses deep learning to extract features automatically, which greatly improves efficiency, simplifies operation, and opens a new era of object detection research. First, this paper provides an overview of deep learning-based object detection backbone networks, reviews and analyzes milestone object detection algorithms, compares commonly used datasets, summarizes applications, and finally concludes the paper. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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LLaMA-LoRA Neural Prompt engineering:A Deep Tuning Framework for Automatically Generating Chinese Text Logical Reasoning Thinking Chains

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Data Intelligence 2024年第2期6卷 375-408页

作者： Songlin Chen Weicheng Wang Xiaoliang Chen Peng lu Zaiyan Yang Yajun Du School of Computer and Software Engineering Xihua Universitychengdu 610039P.R.china Department of Computer Science and Operations Research University of MontrealMontrealQC H3C3J7Canada College of Artificial intelligence Beijing University of Posts and TelecommunicationsBeijing100876China

The exption of Chinese natural language processing(NLP)has stimulated research in the broader NLP ***,existing large language models have limitations in comprehending and reasoning in *** paper addresses these limitations by enhancing Chinese language models comprehension and reasoning capabilities while minimizing resource *** propose LLaMA-LoRA,a neural prompt engineering framework that builds upon the LLaMA-13B model and incorporates the Low-Rank Adaptation(LoRA)of Large Language Models technique for ***-of-Thought(CoT)are crucial for generating intermediate reasoning chains in language models,but their effectiveness can be limited by isolated language *** reasoning resulting from conventional prompts negatively impacts model *** prompts are introduced to encourage reasoning chain generation and accurate answer *** the model with an extensive corpus of Chinese CoT data enhances its comprehension and reasoning *** LLaMA-LoRA model demonstrates exceptional performance across numerous Chinese language tasks,surpassing benchmark performance achieved by related language models such as GPT-3.5,Chat-GLM,and OpenAssistant,delivering accurate,comprehensive,and professional *** availability of our open-source model code facilitates further research in the field of Chinese text logical reasoning thinking chains.

关键词： Chinese natural language processing Neural prompt engineering Large language models Low-Rank adaptation Chain-of-thought

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