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Efficient parallel viterbi algorithm for big data in a spark cloud computing environment 4th

Efficient parallel viterbi algorithm for big data in a spark...

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4th international conference on Innovative data Communication Technologies and Application, ICIDCA 2022

作者： Sassi, Imad Reda, Oumaima Anter, Samir Zellou, Ahmed FSTM Hassan II University Casablanca Morocco ENSIAS Mohammed v University Rabat Morocco

the aim of this paper is to present a parallel distributed version of Viterbi algorithm that combines the advantages of Spark, the big data framework, and hidden Markov models to solve the decoding problem for large scale multidimensional data. the scope of the paper includes a review of hidden Markov models, a study of decoding problem, a presentation of related work, and a discussion of previously proposed implementations. the main part of the paper consists of a description of development and implementation of a parallel distributed Viterbi algorithm in a cloud computing environment, followed by a description of evaluation experiments of the presented algorithm. the results showed that the proposed algorithm is faster, with high scalability and no deterioration in forecast accuracy is observed. © 2023 the Authors. Published by Elsevier B.V.

关键词： Big data

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Dinov2_Mask R-CNN: Self-supervised Instance Segmentation of Diabetic Foot Ulcers 4th

Dinov2_Mask R-CNN: Self-supervised Instance Segmentation of ...

引用

4th Diabetic Foot Ulcers Grand Challenge, DFUC 2024 held in Conjunction 27th international conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2024

作者： Ban, Yun ji Lee, Seihyoung Park, Jiwon Kim, Jeong Eun Kang, Hyun Seo Han, Seungwan Electronics and Telecommunications Research Institute Gwangju Korea Republic of School of Computer Science and Engineering Mokpo National University Muan-Gun Korea Republic of

ISBN: (纸本)9783031808708

Diabetic foot ulcers (DFUs) are a significant condition where early detection is crucial due to the steadily increasing global prevalence of affected patients. In this paper, we aim to assist in the early detection and treatment of DFU by performing instance segmentation on DFU images using a self-supervised method. Using the pre-trained weights of Dinov2, which is based on a self-supervised learning approach, we connected a Mask R-CNN model to Dinov2’s head for instance segmentation. We divide the images provided by the DFU challenge into training and validation sets, with data augmentation applied to enhance performance. the performance of the model was evaluated using Dice score and IoU metrics. On our generated validation dataset, which consists of 400 images, we achieved a maximum Dice score of 0.8181. On the leaderboard, where performance was measured using the Test set provided by DFUC2024, the highest Dice score was 0.6764. By leveraging the self-supervised learning backbone to capture intrinsic features of the images, we achieved superior performance compared to traditional instance segmentation methods. the model and code we have implemented are publicly available at https://***/seihyoung/dfuc2024. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

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Knowledge transfer for Object Detection with Evolution architecture search 4

Knowledge transfer for Object Detection with Evolution archi...

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4th international conference on data-Driven Optimization of Complex Systems, DOCS 2022

作者： Li, Jiaquan Hong, Haokai Shi, Minghui Lin, Qiuzhen Zhou, Fenfen Tan, Kay Chen Jiang, Min Xiamen University School of Informatics Fujian China Shenzhen University College of Computer Science and Software Engineering Guangdong China Fujian Longjing Environment Protection Co. Ltd Fujian China Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

Deep learning has been proved to achieve excellent results in various fields, and appropriate network architecture and sufficient data play an important role. Due to the high cost of annotation for the task of object detection, domain adaptation methods have been introduced in this field. But these methods are based on the rigid network architecture and bound to the input dimension of the adaptive module, which is not only difficult to better balance accuracy and speed of detection model, but also can not use the multi-scale training method, resulting in the reduction of the application scenario of the model. Inspired by this problem, we propose a new object detection method based on multi-scale adversarial domain adaptation and network architecture search. An evolutionary algorithm is adopted to help search the network architecture to balance accuracy and speed. the ability of domain adaptation can also be effectively improved by the searched architecture. the experimental results have demonstrated the significant improvement that benefited from the framework in terms of its performance and computational efficiency in solving unlabeled object detection. © 2022 IEEE.

关键词： Network architecture

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TCP Performance Enhancement Using Cross-Layer Method in IoT Networks 4

TCP Performance Enhancement Using Cross-Layer Method in IoT ...

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4th international conference on Advances in computing, Communication Control and Networking, ICAC3N 2022

作者： Parween, Sultana Hussain, Syed Zeeshan Jamia Millia Islamia Department of Computer Science New Delhi India

ISBN: (纸本)9781665474368

Transmission Control Protocol also known as TCP, utilizes many paths for the simultaneous transmission of data in order to increase its overall performance. But, the previous TCP had a problem to transmit a larger number of subflows in the Internet of things (IoT) networks. We used a cross-layer design to enhance TCP performance in the IoT networks by implementing effective packet scheduling & congestion control in order to address the existing issues. the IoT network uses grid topology and Manhattan distance to improve scalability and flexibility. After network configuration, packet scheduling is conducted using proportional fair scheduling with fitness mechanism (FBPF) and choosing best subflow to minimize transmission latency. Scheduled subflows are sent along optimal paths to increase goodput and throughput. After packet scheduling, the proposed algorithm is used to control congestion in TCP. this algorithm consists of three stages: congestion detection stage, fast retransmission, and fast recovery. To improve fast retransmission time, we utilize a cat and mouse based optimization algorithm (CMBO). the adoption of a metaheuristics algorithm improves the convergence speed thereby improving the user throughput. Simulation findings indicate that our proposed scheme outperforms high TCP performance with respect to goodput, throughput, packet loss, congestion window size, and transmission delay. © 2022 IEEE.

关键词： Internet of things

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Folk melody generation based on CNN-BiGRU and Self-Attention 4

Folk melody generation based on CNN-BiGRU and Self-Attention

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4th international conference on Communications, Information System and Computer Engineering, CISCE 2022

作者： Su, Yuping Han, Rong Wu, Xiaojun Zhang, Yumei Li, Yue Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism School of Computer Science Shaanxi Normal University Xi'an China

ISBN: (数字)9781665498487

ISBN: (纸本)9781665498487

Using various deep learning networks to generate music is a research hotspot in the field of human intelligence. Due to the limitation of network structure in the existing melody generation models, the quality of melody generation is poor. In this paper, for folk melody, we propose a melody generation network based on CNN-BiGRU and Self-Attention. First, the processed data is input into the CNN module consisting of one-dimensional convolution layer and pooling layer to extract local features. then, the features extracted by CNN module are constructed as time series and is input into BiGRU network to extract global features. Finally, Self-Attention assigns different weights to the features of BiGRU output to highlight important feature information during note generation. Experimental results show that the prediction accuracy of the proposed model is improved by 3.61 % compared with BiGUR, 5.26% compared with CNN, and 6.53% compared with CNN-BiGRU. Meanwhile, the model presented in this paper also achieves improvement in other evaluation measures. © 2022 IEEE.

关键词： Convolutional neural networks

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Towards an Understanding of the Lipophilicity of Non-Coded Amino Acids: Computational Simulations of Proline Analogs 4

Towards an Understanding of the Lipophilicity of Non-Coded A...

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4th IEEE international conference on BioInspired Processing, BIP 2022

作者： Matamoros, Paulina Pinheiro, Silvana Viayna, Antonio Zamora, William School of Chemistry CBio3 Laboratory San José San Pedro Costa Rica University of Costa Rica Faculty of Pharmacy CBio3 Laboratory San José San Pedro Costa Rica University of Barcelona Faculty of Pharmacy and Food Sciences Department of Nutrition Food Science and Gastronomy Santa Coloma de Gramenet Spain San José San Pedro Costa Rica

ISBN: (纸本)9781665470483

Amino acids are the backbone for the formation of biopolymers known as proteins. One of them, proline, can be found in high concentrations in collagen, being this protein the most supportive for the skin, tendons, bones, and connective tissue, promoting their health and recovery. Here, we compute the lipophilicity of 25 non-coded proline analogs using the quantum mechanics implicit solvation model SMD. Comparison with the experimental data provided for partition coefficients yielded a root-mean square error (rmse) of 0.72 (log P units). Overall, the results support the appropriateness of SMD solvation model for computing the n-octanol/water partition coefficient in proline analogs, which can be used to tune the hydrophobic properties of bioinspired synthetic peptides. © 2022 IEEE.

关键词： Amino acids

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Image Quality Enhancement by format-specific Digital Object Tracking 4

Image Quality Enhancement by format-specific Digital Object ...

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4th international conference on Cognitive computing and Information Processing, CCIP 2022

作者： Maheswari, G. Remya, K. Reenadevi, R. Surendiran, J. Prasad, Sanjana Sathish, K. Hindustan University Department of Informatioon Techhnology Chennai India Sona College of Technology Department of Computer Science and Engineering Salem India HKBK College of Engineering Department of Electronics and Communication Engineering Bangalore India

ISBN: (纸本)9781665456487

In general, selecting an image format is crucial for deciding what kind of results to display in picture searches. Among the most significant discoveries about RAW and JPEG formats, this is. Even yet, most experts in the field are able to tell the difference between the two. Images in JPEG format are widely used because of their quality and convenience. Images are often saved in JPG format before being printed or uploaded online. there are now worries regarding JPEGs' visual quality. this is the most used picture compression format, thus it's required by the great majority of digital cameras. However, this alters the photos irreversibly, rendering them unrecognisable. Selecting an appropriate picture format is the primary challenge here. this research proposed a smart format improved object tracking approach to boost digital image quality. By default, the camera is set to reduce the size of the captured image files by discarding unnecessary pixels. Depending on your preferences, the compression might be higher or lower. Cameras will reject the bare minimum of data if the file size is set too high. As the number of potential viewers increases, the resolution drops. the suggested method is effective in improving the compression ratio of items. © 2022 IEEE.

关键词： Digital cameras

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A Deep Q-Learning-Based Adaptive Traffic Light Control System for Urban Safety 4

A Deep Q-Learning-Based Adaptive Traffic Light Control Syste...

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4th international conference on Advances in computing, Communication Control and Networking, ICAC3N 2022

作者： Tigga, Ashish Hota, Lopamudra Patel, Sanjeev Kumar, Arun National Institute of Technology Department of Computer Science and Engineering Rourkela769008 India

ISBN: (纸本)9781665474368

Traffic congestion is a significant and recurring issues in today's urbanised world, caused by an increase in the number of vehicles. While vehicle density fluctuates on temporally short and geographically small scales, efficient traffic signaling system helps in avoiding traffic congestion. An inefficacious traffic system can lead to congestion and delays resulting in high pollution, and fuel wastage. the Deep Reinforcement Learning (DRL) method provides an excellent approach to solve the problem involving complex relations such as traffic flow and congestion. Recent development in Deep Neural Network (DNN) further enhances the learning capabilities of an agent with complex real-time data. the paper presents an intelligent Traffic Light Control System (TLCS) built on a Deep Q-Learning (DQL) model that accurately represents the problem's components: agents, environment, and actions. the proposed model aims to minimize the traffic queue length and delay in terms of waiting time. the model is implemented using Simulation of Urban MObility (SUMO) for traffic generation in an urban scenario. the performance of the proposed model is compared with a traditional traffic light control system. the simulation results show that the proposed DQL-based model can significantly reduce the delay compared with the traditional model. © 2022 IEEE.

关键词： Traffic congestion

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Deep Learning Model to Detect HTTP-Based Attack on Internet of things 4th

Deep Learning Model to Detect HTTP-Based Attack on Internet ...

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4th international conference on data, Engineering, and Applications, IDEA 2022

作者： Dhillon, Sumeet Mishra, Nishchol Shakya, Devendra Kumar Department of Computer Science and Engineering SATI Vidisha India School of Information Technology RGPV Bhopal India Department of Electronics Engineering SATI Vidisha India

ISBN: (纸本)9789819700363

the information exchange between devices and remote servers occurs through the Application Programming Interface (API), which was created based on Internet technology (commonly referred to as web technology), in most of edge computing applications, including Internet of things (IoT) devices. the majority of users cannot use the edge device APIs directly;hence, they experience relatively fewer threats than users of typical web applications. However, as IoT devices have gained popularity, attacks targeting APIs have started to acquire attraction. In order to identify online traffic received by IoT services and mine dangerous traffic, this paper provides a web attack vector identification approach that will deliver security information to the Security Operation Center (SOC). this approach is based on the feature extraction, targeting the mostly constant message format of the API request, in combination with the Bidirectional Long Short-Term Memory (BLSTM) attack vector identification for online traffic. the experimental findings demonstrate that, in comparison to standard machine learning approaches and rule-based web application firewalls (WAF), the suggested approach is more successful at identifying attacks against IoT service APIs. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： HTTP

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Chinese Spelling Error Correction Based on Adaptive Pre-training and Multi-Task Learning 4

Chinese Spelling Error Correction Based on Adaptive Pre-trai...

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4th international conference on Applied Machine Learning, ICAML 2022

作者： Li, Wei Lin, Zhongyao School of Computer Science School of Business Central South University Changsha China

ISBN: (纸本)9781665462655

With the rapid development of computer application technology, more and more information is transmitted through the text through the network. the number of network texts grows fast, and it is difficult to guarantee the quality of the text. Low-quality information will pollute the network environment and spread error information. the traditional manual text check method has been unable to deal with today's massive data. therefore, spelling error correction methods based on the deep learning model have attracted the attention of many experts and scholars in recent years. this paper proposes a Chinese spelling error correction model based on task adaptive pre-training and multi-task learning. Firstly, based on the Bert, this paper proposes an optimized mask strategy for the pre-training method to make the pre-training model better match the spelling error correction task. Secondly, based on the language model obtained by task adaptive pre-training, this paper combines spelling error detection and spelling error correction by multi-task learning, Finally, we test our model on the SIGHAN dataset, which is a general benchmark dataset of Chinese spelling error correction. Compared with other models, our model outperforms all competitor methods. © 2022 IEEE.

关键词： Error correction

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