2023 28th International Computer Conference, Computer Society of Iran (CSICC)最新文献_第4页

Henry V: A linear time algorithm for solving the N-Queens Problem using only 5 patterns 亨利五世:一个线性时间算法解决n皇后问题，只使用5个模式

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105390

A. Dehghani, Reza Namvar, Abdullah Khalili

引用次数: 0

Predicting the neural response of primary visual cortex (v1) using deep learning approach 用深度学习方法预测初级视觉皮层(v1)的神经反应

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105321

Sajjad Abdi Dehsorkh, Reshad Hosseini

{"title":"Predicting the neural response of primary visual cortex (v1) using deep learning approach","authors":"Sajjad Abdi Dehsorkh, Reshad Hosseini","doi":"10.1109/CSICC58665.2023.10105321","DOIUrl":"https://doi.org/10.1109/CSICC58665.2023.10105321","url":null,"abstract":"One of the most important parts of the brain is the visual cortex, which receives data from the visual system as input and, by a hierarchical processing, leads to our understanding of the scene. Despite the efforts in recent years, predicting the neural response to the natural stimuli by the best models proposed for the primary visual cortex has not performed well to date. However, the results obtained from machine learning show that deep neural networks are able to learn nonlinear functions to process visual information. In this study, a new approach to modeling V1 neural activity is presented which is based on the VGG-19 deep network. In this approach, inspired by the function of the visual cortex of the brain, a structure is introduced that by adding a convolutional network causes the model to pay attention to important parts of the input. In this structure, a mask is created using the receptive field of the brain neurons and is added to the middle layers of the deep network. The use of this mask makes the network to be more influenced by the areas of the image to which brain neurons are more sensitive. The proposed deep network training shows that the results obtained from predicting the neural response to natural stimuli are faster and more accurate than previous models.","PeriodicalId":127277,"journal":{"name":"2023 28th International Computer Conference, Computer Society of Iran (CSICC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126948874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quantizing YOLOv7: A Comprehensive Study 量化YOLOv7:一项综合研究

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105310

Mohammadamin Baghbanbashi, Mohsen Raji, B. Ghavami

{"title":"Quantizing YOLOv7: A Comprehensive Study","authors":"Mohammadamin Baghbanbashi, Mohsen Raji, B. Ghavami","doi":"10.1109/CSICC58665.2023.10105310","DOIUrl":"https://doi.org/10.1109/CSICC58665.2023.10105310","url":null,"abstract":"YOLO is a deep neural network (DNN) model presented for robust real-time object detection following the one-stage inference approach. It outperforms other real-time object detectors in terms of speed and accuracy by a wide margin. Nevertheless, since YOLO is developed upon a DNN backbone with numerous parameters, it will cause excessive memory load, thereby deploying it on memory-constrained devices is a severe challenge in practice. To overcome this limitation, model compression techniques, such as quantizing parameters to lower-precision values, can be adopted. As the most recent version of YOLO, YOLOv7 achieves such state-of-the-art performance in speed and accuracy in the range of 5 FPS to 160 FPS that it surpasses all former versions of YOLO and other existing models in this regard. So far, the robustness of several quantization schemes has been evaluated on older versions of YOLO. These methods may not necessarily yield similar results for YOLOv7 as it utilizes a different architecture. In this paper, we conduct in-depth research on the effectiveness of a variety of quantization schemes on the pre-trained weights of the state-of-the-art YOLOv7 model. Experimental results demonstrate that using 4-bit quantization coupled with the combination of different granularities results in ~3.92x and ~3.86x memory-saving for uniform and non-uniform quantization, respectively, with only 2.5% and 1% accuracy loss compared to the full-precision baseline model.","PeriodicalId":127277,"journal":{"name":"2023 28th International Computer Conference, Computer Society of Iran (CSICC)","volume":"1209 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121052318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Using Structured Pruning to Find Winning Lottery Tickets 使用结构化修剪来发现中奖彩票

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105376

Kamyab Azizi, H. Taheri, Soheil Khooyooz

引用次数: 0

A Secure and Efficient Scheme for Mutual Authentication for Integrity of Exchanged Data in IoMT 一种安全有效的IoMT交换数据完整性相互认证方案

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105398

Mahmoud Faraji, H. Shahriari, Mahdi Nikooghadam

{"title":"A Secure and Efficient Scheme for Mutual Authentication for Integrity of Exchanged Data in IoMT","authors":"Mahmoud Faraji, H. Shahriari, Mahdi Nikooghadam","doi":"10.1109/CSICC58665.2023.10105398","DOIUrl":"https://doi.org/10.1109/CSICC58665.2023.10105398","url":null,"abstract":"The emergence of pandemic diseases like Covid-19 in recent years has made it more important for Internet of Medical Things (IoMT) environments to build contact between patients and doctors in order to control their health state. Patients will be able to send their healthcare data to the cloud server of the medical service provider in remote medical environments through sensors connected to their smart devices, such as watches or smartphones. However, patients’ worries surrounding their data privacy protection are still present. In order to ensure the security and privacy of patients’ healthcare data in remote medical environments, a number of different schemes have been proposed by researchers. However, these schemes have not been able to take all security requirements into account. Consequently, in this study, we have proposed a secure and effective protocol to safeguard the privacy of patients' medical data when it is sent to the server. This protocol entails two components: mutual authentication of the patient and the server of the medical service provider, as well as the integrity of the exchanged data. Also, our scheme satisfies security requirements and is resistant to well-known attacks. Following this, we used the Scyther tool to formally analyze our proposed scheme. The results showed that the scheme is secure, and in the section on performance analysis, we demonstrated that the proposed scheme performs better than comparable schemes.","PeriodicalId":127277,"journal":{"name":"2023 28th International Computer Conference, Computer Society of Iran (CSICC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116948420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Unsupervised Photoacoustic Tomography Image Reconstruction from Limited-View Unpaired Data using an Improved CycleGAN 使用改进的CycleGAN从有限视点未配对数据中重建无监督光声断层成像图像

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105363

Hesam Hakimnejad, Z. Azimifar, Mohammad Sadegh Nazemi

{"title":"Unsupervised Photoacoustic Tomography Image Reconstruction from Limited-View Unpaired Data using an Improved CycleGAN","authors":"Hesam Hakimnejad, Z. Azimifar, Mohammad Sadegh Nazemi","doi":"10.1109/CSICC58665.2023.10105363","DOIUrl":"https://doi.org/10.1109/CSICC58665.2023.10105363","url":null,"abstract":"Photoacoustic tomography (PAT) is a hybrid imaging method with great applications in preclinical research and clinical applications. However, due to the limited-view issue, it is often hard to cover the desired tissue completely, thus resulting in severe artifacts in reconstructed images. Enhancing a reconstructed image to become artifact-free could be considered an image-to-image translation task which is addressed easily by the well-known Pix2Pix generative adversarial network (GAN). Training Pix2Pix usually requires a large paired dataset. Preparing such datasets can be difficult or even in some cases impossible. In this paper, we propose an improved unsupervised reconstruction method based on cycle-consistent adversarial networks (CycleGAN), to overcome the need for paired datasets. CycleGAN can learn image-to-image translation tasks from an unpaired dataset without the need for one-to-one matching between low-quality and high-quality images. Experimental results demonstrate that the proposed architecture outperforms the original CycleGAN in terms of image similarity metrics including PSNR and SSIM.","PeriodicalId":127277,"journal":{"name":"2023 28th International Computer Conference, Computer Society of Iran (CSICC)","volume":"33 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125714281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Ontology-based Approach to Facilitate Semantic Interoperability of Context-Aware Systems 一种基于本体的促进上下文感知系统语义互操作性的方法

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105364

Hamed Barangi, Shekoufeh Kolahdouz Rahimi, B. Zamani, Hossein Moradi

{"title":"An Ontology-based Approach to Facilitate Semantic Interoperability of Context-Aware Systems","authors":"Hamed Barangi, Shekoufeh Kolahdouz Rahimi, B. Zamani, Hossein Moradi","doi":"10.1109/CSICC58665.2023.10105364","DOIUrl":"https://doi.org/10.1109/CSICC58665.2023.10105364","url":null,"abstract":"Semantic interoperability is one of the most critical challenges for software developers while integrating two or more context-aware systems. In such circumstances, it is essential to understand the meaning and interpretation of various contexts in different business domains and to align context ontologies together. Our investigations reveal that existing works poorly address these requirements. To fill this gap, this paper proposes the Ontology as a Service (OaaS) approach to facilitate the semantic interoperability of context-aware systems. In the proposed solution, the complexities of semantic interoperability are resolved and handled by a standalone ontology service, which can be easily reused and consumed by different ontology service consumers and brokers. The proposed ontology service includes several ontology repositories, a web service that positions a context concept in the existing ontologies and another web service that maps the relationship between existing context concepts. We evaluated our approach with a case study that resolves three semantic differences between two IoT applications originating from heterogeneous domains of smart home and health environments.","PeriodicalId":127277,"journal":{"name":"2023 28th International Computer Conference, Computer Society of Iran (CSICC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125945135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deceptive review detection using GAN enhanced by GPT structure and score of reviews 基于GPT结构和评论分数增强的GAN欺骗性评论检测

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105368

Maryam Tamimi, Mostafa Salehi, S. Najari

{"title":"Deceptive review detection using GAN enhanced by GPT structure and score of reviews","authors":"Maryam Tamimi, Mostafa Salehi, S. Najari","doi":"10.1109/CSICC58665.2023.10105368","DOIUrl":"https://doi.org/10.1109/CSICC58665.2023.10105368","url":null,"abstract":"These days, in online E-commerce platforms, the most of users utilize the purchase experience of other users reported in the reviews to make a right decision. As much as the positive effect of this phenomena, there would be a motivation to produce deceptive reviews toward some malicious goals. Therefore, the detection and deletion of these deceptive reviews from online platforms will be an essential task to keep them safe. Different approaches from Machine Learning (ML)-based methods to the recent Neural Networks (NN)-based ones have been proposed to detect deceptive reviews. Here, the lack of sufficient labeled data is an essential barrier, to obviate that Generative Adversarial Networks (GAN) have been utilized to generate some data with a distribution close to original ones. In this regard and along with the successful results of Generative Pre-trained Transformers (GPT) in textual tasks, it have also been used besides GAN framework to detect deceptive reviews. Despite a lot of efforts on this matter, there is no efficient study for considering metadata or behavioral features besides powerful generative models. In this paper, we have proposed a new approach called Score_Gpt2ganto consider the scores of reviews as a regularization concept besides the GPT-based GAN approach. Evaluation results in comparison between different methods have shown an increase in the accuracy of 1.4% on the TripAdvisor dataset and 3.8% on the YelpZip dataset by our proposed method.","PeriodicalId":127277,"journal":{"name":"2023 28th International Computer Conference, Computer Society of Iran (CSICC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115011862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SDGC: Software Defined Game Clustering for Jitter Optimization SDGC:用于抖动优化的软件定义游戏聚类

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105396

Amirmahdi Ajoodani, Mohammadreza Ghafari, Emma Dennis-Knieriem

引用次数: 0

Unsupervised Broadcast News Summarization; a Comparative Study on Maximal Marginal Relevance (MMR) and Latent Semantic Analysis (LSA) 无监督广播新闻摘要;最大边际关联(MMR)和潜在语义分析(LSA)的比较研究

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-05 DOI: 10.1109/CSICC58665.2023.10105403

Majid Ramezani, Mohammad-Salar Shahryari, Amir-Reza Feizi-Derakhshi, M. Feizi-Derakhshi

引用次数: 1