2023 28th International Computer Conference, Computer Society of Iran (CSICC)最新文献_第3页

Identification Important Nodes in Diffusion Process by User Experience on Social Network 利用用户体验识别社交网络扩散过程中的重要节点

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105397

F. Kazemzadeh, A. Safaei, M. Mirzarezaee

{"title":"Identification Important Nodes in Diffusion Process by User Experience on Social Network","authors":"F. Kazemzadeh, A. Safaei, M. Mirzarezaee","doi":"10.1109/CSICC58665.2023.10105397","DOIUrl":"https://doi.org/10.1109/CSICC58665.2023.10105397","url":null,"abstract":"The influence maximization problem (IMP) has been proposed in social networks. Nowadays, it is considered an important and practical problem due to the earnings potential by identifying a set of influential nodes, and therefore, it has been attracted by many researchers. This problem seeks to identify a set with K nodes among the social network nodes to maximize the influence and diffusion of information in that community. Algorithms proposed by other researchers have many shortcomings in terms of accuracy and run time of the algorithm. Hence, this article aimed to find the best, most accurate, and fastest solution to the problem.The article presented the UXM algorithm and used the User Experience criterion for the first time to solve this problem. At first, taking into account the reach club phenomenon and using criteria such as clustering coefficient, degree and also using user experience, nodes with more influence have been selected as the primary candidate set. Then, according to the component nodes, K final influential nodes have been selected. In this way, it could identify the set of nodes as accurately as possible with high efficiency in the shortest possible time. The evaluation of this algorithm and its comparison with other algorithms indicated excellent results in terms of run time and accuracy in selecting the set of nodes by the proposed algorithm.","PeriodicalId":127277,"journal":{"name":"2023 28th International Computer Conference, Computer Society of Iran (CSICC)","volume":"98 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":"122587114","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

Analyzing the Use of Auditory Filter Models for Making Interpretable Convolutional Neural Networks for Speaker Identification 分析使用听觉滤波模型制作可解释卷积神经网络用于说话人识别

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105387

Hossein Fayyazi, Y. Shekofteh

{"title":"Analyzing the Use of Auditory Filter Models for Making Interpretable Convolutional Neural Networks for Speaker Identification","authors":"Hossein Fayyazi, Y. Shekofteh","doi":"10.1109/CSICC58665.2023.10105387","DOIUrl":"https://doi.org/10.1109/CSICC58665.2023.10105387","url":null,"abstract":"Most of the advances in artificial intelligence are based on understanding the function of different parts of the living organism's body. High complexity of some body parts, e.g., the brain, leads to using some abstractions for making intelligent models, which can make the models uninterpretable. This general process can be seen in the development of Deep Neural Networks (DNNs). Although DNNs are models with high performance, they have a black-box nature which makes them unreliable in some applications such as medicine. Fortunately, nature can again help to make DNN models explainable. The use of meaningful filters in the first layer of Convolutional Neural Networks (CNNs) is a successful attempt in this field. The goal of this paper is to examine the use of some auditory filter models as CNN front-ends to make them interpretable and then to evaluate the resulting filter banks in the Speaker Identification (SID) task. Results confirm the previous knowledge about the filtering mechanism of the auditory system. This simple observation can lead to an abstract conclusion that making a complex learning model interpretable, specifically using simple elements inspired by nature, can disclose the hidden aspects of how the human body works. Moreover, replicating the essential functions of the human auditory system leads to better model performance.","PeriodicalId":127277,"journal":{"name":"2023 28th International Computer Conference, Computer Society of Iran (CSICC)","volume":"140 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":"116038150","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

OutCLIP, A New Multi-Outfit CLIP Based Triplet Network OutCLIP，一种新的基于多装备CLIP的三重网络

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105384

Zahra Haghgu, R. Azmi, Lachin Zamani, Fatemeh Moradian

引用次数: 0

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