2021 IEEE/ACS 18th International Conference on Computer Systems and Applications (AICCSA)最新文献_第2页

Downsampling Attack on Automatic Speaker Authentication System 自动说话人认证系统的降采样攻击

2021 IEEE/ACS 18th International Conference on Computer Systems and Applications (AICCSA) Pub Date : 2021-11-01 DOI: 10.1109/AICCSA53542.2021.9686767

S. Asha, P. Vinod, Varun G. Menon, A. Zemmari

{"title":"Downsampling Attack on Automatic Speaker Authentication System","authors":"S. Asha, P. Vinod, Varun G. Menon, A. Zemmari","doi":"10.1109/AICCSA53542.2021.9686767","DOIUrl":"https://doi.org/10.1109/AICCSA53542.2021.9686767","url":null,"abstract":"Recent years have observed an exponential growth in the popularity of audio-based authentication systems. The benefit of a voice-based authentication system is that the person need not be physically present. Voice biometric system provides effective authentication in various domains like remote access control, authentication in mobile applications, customer care centers for call attests. Most of the existing authentication systems that recognize speakers formulate deep learning models for better classification. At the same time, research studies show that deep learning models are highly vulnerable to adversarial inputs. A breach in security on authentication systems are not generally acceptable. This paper exposes the vulnerabilities of audio-based authentication systems. Here, we propose a novel downsampling attack to the speaker recognition system. This attack can effectively trick the speaker recognition framework by causing inaccurate predictions. The proposed threat model achieved remarkable attack effectiveness of 75%. This system employs a custom human voice dataset recorded in real-time conditions to achieve real-time effectiveness during classification. We compare the attack accuracy of the proposed attack against the adversarial audios generated using the CleverHans toolbox. The proposed attack being a black box attack, is transferable to other deep learning systems also.","PeriodicalId":423896,"journal":{"name":"2021 IEEE/ACS 18th International Conference on Computer Systems and Applications (AICCSA)","volume":"322 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116295601","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

Message from the AICCSA 2021 General Chairs AICCSA 2021年总主席致辞

2021 IEEE/ACS 18th International Conference on Computer Systems and Applications (AICCSA) Pub Date : 2021-11-01 DOI: 10.1109/aiccsa53542.2021.9686908

引用次数: 0

Slice-Level-Guided Convolutional Neural Networks to study the Right Ventricular Segmentation using MRI Short-Axis sequences 切片水平引导卷积神经网络研究MRI短轴序列右心室分割

2021 IEEE/ACS 18th International Conference on Computer Systems and Applications (AICCSA) Pub Date : 2021-11-01 DOI: 10.1109/AICCSA53542.2021.9686842

Asma Ammari, R. Mahmoudi, B. Hmida, R. Saouli, M. Hedi

{"title":"Slice-Level-Guided Convolutional Neural Networks to study the Right Ventricular Segmentation using MRI Short-Axis sequences","authors":"Asma Ammari, R. Mahmoudi, B. Hmida, R. Saouli, M. Hedi","doi":"10.1109/AICCSA53542.2021.9686842","DOIUrl":"https://doi.org/10.1109/AICCSA53542.2021.9686842","url":null,"abstract":"The cardiac right ventricle has a vital role in the cardiac cycle. To assess its function using Magnetic Resonance Imaging (MRI), the segmentation is an important task, but it is challenged by the complex shape of this cavity, its thin borders, and shape variability. Accordingly, several approaches have been proposed to overcome these issues. Yet, a significant divergence of precision still appears among the spatial slices. In this paper, we attempt to study the impact of short-axis slices from base to apex on the segmentation process. First, a comparative study is enabled to assess the segmentation quality among these slices using a U-Net- based convolutional neural network. Two public labelled datasets are exploited with our prepared data to allow the training process. The dice-coefficient assessment of each slice-level exhibits a significant accuracy decrease for the basal and apical slices. Next, a personalized investigation is carried out for each slice level apart. Accordingly, three sub-sets are retrieved from the initial training set regrouping slices into basal, central, and apical. Furthermore, to monitor the segmentation behaviour using these sub-datasets, different U-Net-based models are trained and evaluated. The obtained results show that the central slices scores enhanced from 0.87 to 0.92 using slice-level based. On the other hand, basal and apical slices obtained higher results using the global dataset.","PeriodicalId":423896,"journal":{"name":"2021 IEEE/ACS 18th International Conference on Computer Systems and Applications (AICCSA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132149125","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: 2

Audio representations for deep learning in sound synthesis: A review 声音合成中深度学习的音频表示:综述

2021 IEEE/ACS 18th International Conference on Computer Systems and Applications (AICCSA) Pub Date : 2021-11-01 DOI: 10.1109/AICCSA53542.2021.9686838

Anastasia Natsiou, Seán O'Leary

{"title":"Audio representations for deep learning in sound synthesis: A review","authors":"Anastasia Natsiou, Seán O'Leary","doi":"10.1109/AICCSA53542.2021.9686838","DOIUrl":"https://doi.org/10.1109/AICCSA53542.2021.9686838","url":null,"abstract":"The rise of deep learning algorithms has led many researchers to withdraw from using classic signal processing methods for sound generation. Deep learning models have achieved expressive voice synthesis, realistic sound textures, and musical notes from virtual instruments. However, the most suitable deep learning architecture is still under investigation. The choice of architecture is tightly coupled to the audio representations. A sound’s original waveform can be too dense and rich for deep learning models to deal with efficiently -and complexity increases training time and computational cost. Also, it does not represent sound in the manner in which it is perceived. Therefore, in many cases, the raw audio has been transformed into a compressed and more meaningful form using upsampling, feature-extraction, or even by adopting a higher level illustration of the waveform. Furthermore, conditional on the form chosen, additional conditioning representations, different model architectures, and numerous metrics for evaluating the reconstructed sound have been investigated. This paper provides an overview of audio representations applied to sound synthesis using deep learning. Additionally, it presents the most significant methods for developing and evaluating a sound synthesis architecture using deep learning models, always depending on the audio representation.","PeriodicalId":423896,"journal":{"name":"2021 IEEE/ACS 18th International Conference on Computer Systems and Applications (AICCSA)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132200880","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: 7

The Emerging Role of Deep Learning in Multimodality Medical Imaging 深度学习在多模态医学成像中的新兴作用

2021 IEEE/ACS 18th International Conference on Computer Systems and Applications (AICCSA) Pub Date : 2021-11-01 DOI: 10.1109/aiccsa53542.2021.9686868

引用次数: 0

Modeling Big Data-centric Services using Knowledge Graphs 使用知识图建模以大数据为中心的服务

2021 IEEE/ACS 18th International Conference on Computer Systems and Applications (AICCSA) Pub Date : 2021-11-01 DOI: 10.1109/AICCSA53542.2021.9686922

Fedia Ghedass, Faouzi Ben Charrada

{"title":"Modeling Big Data-centric Services using Knowledge Graphs","authors":"Fedia Ghedass, Faouzi Ben Charrada","doi":"10.1109/AICCSA53542.2021.9686922","DOIUrl":"https://doi.org/10.1109/AICCSA53542.2021.9686922","url":null,"abstract":"Big services have recently emerged from the synergy between big data and cloud computing paradigms. This new big data-centric service model aims to provide customer-oriented massive services by combining both physical and virtualized resources from different domains. Although such complex ecosystem is able to process, encapsulate and offer huge volumes of data as services, its management operations are beyond the ability of human administrators, due to several challenges including the big services’ large-scale nature and complexity, the heterogeneity of their components (e.g., services, data sources, connected things), the dynamicity and uncertainty of their hosting cloud environments. To cope with the lack of understanding regarding big services capabilities, we propose to describe them using a novel meta-model for the quality of big services (QoBS). We also take advantage of a recent technology called knowledge graphs, to represent the big service information (service descriptions, services’ and data sources’ quality levels, management policies) as a heterogeneous information network. Finally, a multi-view representation learning approach is proposed to infer additional knowledge regarding big services capabilities.","PeriodicalId":423896,"journal":{"name":"2021 IEEE/ACS 18th International Conference on Computer Systems and Applications (AICCSA)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123351923","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

A comparative study on the importance of each face part in facial gender recognition via convolutional neural networks 卷积神经网络在人脸性别识别中面部各部分重要性的比较研究

2021 IEEE/ACS 18th International Conference on Computer Systems and Applications (AICCSA) Pub Date : 2021-11-01 DOI: 10.1109/AICCSA53542.2021.9686825

Rahma Amri, A. Gazdar, W. Barhoumi

{"title":"A comparative study on the importance of each face part in facial gender recognition via convolutional neural networks","authors":"Rahma Amri, A. Gazdar, W. Barhoumi","doi":"10.1109/AICCSA53542.2021.9686825","DOIUrl":"https://doi.org/10.1109/AICCSA53542.2021.9686825","url":null,"abstract":"Nowadays, gender recognition systems are very important in several fields such as security, human machine interaction, surveillance and targeted advertising. However, many factors, such as makeup and disguise, can affect recognition and extend the processing time. Our research revolves around this issue. This is a comparative experimental study of the significance of each part of the face (eyes, mouth, nose) in the gender facial recognition via convolutional neural networks (CNN). As a first step our goal is to find the most crucial part of the face in order to determine the most important part in the gender recognition. The used method was tested on the UTKFace dataset and the preliminary results confirm that the eyes contain the most discriminating information regarding gender identification. We achieve a classification accuracy of 92% for eyes, 91% for mouth and 89% for nose. Then we propose a second study on the degree of importance of the eyes for both genders by training the system using only eyes. We achieve a classification accuracy of 99% for eyes of men and 99% for eyes of women.","PeriodicalId":423896,"journal":{"name":"2021 IEEE/ACS 18th International Conference on Computer Systems and Applications (AICCSA)","volume":"208 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122618331","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

On a Small File Merger for Fast Access and Modifiability of Small Files in HDFS 基于小文件合并的HDFS小文件的快速访问和可修改性研究

2021 IEEE/ACS 18th International Conference on Computer Systems and Applications (AICCSA) Pub Date : 2021-11-01 DOI: 10.1109/AICCSA53542.2021.9686873

Di Chen, C. Wu, Wei Shen, Yu Zhang

{"title":"On a Small File Merger for Fast Access and Modifiability of Small Files in HDFS","authors":"Di Chen, C. Wu, Wei Shen, Yu Zhang","doi":"10.1109/AICCSA53542.2021.9686873","DOIUrl":"https://doi.org/10.1109/AICCSA53542.2021.9686873","url":null,"abstract":"Hadoop Distributed File System (HDFS) was originally designed to store big files and has been widely used in big-data ecosystem. However, it may suffer from serious performance issues when handling a large number of small files. In this paper, we propose a novel archive system, referred to as Small File Merger (SFM), to solve small file problems in HDFS. The key idea is to combine small files into large ones and build an index for accessing original files. Unlike traditional archive systems such as Hadoop Archives (Har), SFM allows modification of archived files directly without re-archiving. Considering that most of the reads in HDFS are sequential, we design an adaptive readahead strategy based on the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm to maximize read performance. Furthermore, our system provides an HDFS-compatible interface, which can be used directly without recompiling and redeploying the existing HDFS cluster, hence facilitating convenient deployment for practical use. Preliminary experimental results show that our system achieves better performance than existing methods.","PeriodicalId":423896,"journal":{"name":"2021 IEEE/ACS 18th International Conference on Computer Systems and Applications (AICCSA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114260580","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

PReDIHERO – Privacy-Preserving Remote Deep Learning Inference based on Homomorphic Encryption and Reversible Obfuscation for Enhanced Client-side Overhead in Pervasive Health Monitoring PReDIHERO——基于同态加密和可逆混淆的保护隐私的远程深度学习推断，用于增强普然健康监控中客户端开销

2021 IEEE/ACS 18th International Conference on Computer Systems and Applications (AICCSA) Pub Date : 2021-11-01 DOI: 10.1109/AICCSA53542.2021.9686893

Amine Boulemtafes, A. Derhab, Nassim Ait Ali Braham, Y. Challal

{"title":"PReDIHERO – Privacy-Preserving Remote Deep Learning Inference based on Homomorphic Encryption and Reversible Obfuscation for Enhanced Client-side Overhead in Pervasive Health Monitoring","authors":"Amine Boulemtafes, A. Derhab, Nassim Ait Ali Braham, Y. Challal","doi":"10.1109/AICCSA53542.2021.9686893","DOIUrl":"https://doi.org/10.1109/AICCSA53542.2021.9686893","url":null,"abstract":"Homomorphic Encryption is one of the most promising techniques to deal with privacy concerns, which is raised by remote deep learning paradigm, and maintain high classification accuracy. However, homomorphic encryption-based solutions are characterized by high overhead in terms of both computation and communication, which limits their adoption in pervasive health monitoring applications with constrained client-side devices. In this paper, we propose PReDIHERO, an improved privacy-preserving solution for remote deep learning inferences based on homomorphic encryption. The proposed solution applies a reversible obfuscation technique that successfully protects sensitive information, and enhances the client-side overhead compared to the conventional homomorphic encryption approach. The solution tackles three main heavyweight client-side tasks, namely, encryption and transmission of private data, refreshing encrypted data, and outsourcing computation of activation functions. The efficiency of the client-side is evaluated on a healthcare dataset and compared to a conventional homomorphic encryption approach. The evaluation results show that PReDIHERO requires increasingly less time and storage in comparison to conventional solutions when inferences are requested. At two hundreds inferences, the improvement ratio could reach more than 30 times in terms of computation overhead, and more than 8 times in terms of communication overhead. The same behavior is observed in sequential data and batch inferences, as we record an improvement ratio of more than 100 times in terms of computation overhead, and more than 20 times in terms of communication overhead.","PeriodicalId":423896,"journal":{"name":"2021 IEEE/ACS 18th International Conference on Computer Systems and Applications (AICCSA)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116041974","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: 2

Representation of Smart Contracts as State Diagrams 用状态图表示智能合约

2021 IEEE/ACS 18th International Conference on Computer Systems and Applications (AICCSA) Pub Date : 2021-11-01 DOI: 10.1109/AICCSA53542.2021.9686862

Marina Luiza Lardizabal Vieira, Patrícia Vilain

{"title":"Representation of Smart Contracts as State Diagrams","authors":"Marina Luiza Lardizabal Vieira, Patrícia Vilain","doi":"10.1109/AICCSA53542.2021.9686862","DOIUrl":"https://doi.org/10.1109/AICCSA53542.2021.9686862","url":null,"abstract":"Smart contracts have gained popularity with the emergence of blockchain technology, although the concept behind them has been studied since the 1990s. The automation of contracts signed in real life is an interdisciplinary subject and draws attention not only in the scope of technology but also in areas like business area and legal area. With the aim of providing clear understanding, accuracy and security of information in the process of creating a smart contract, many tools have been developed, either to avoid vulnerabilities or to allow anyone to contribute in writing a contract. In view of this scenario and seeking to further facilitate the general understanding of a smart contract, this paper aims to study the representation of smart contracts as state diagrams. It summarizes, through a systematic mapping, the many ways to visually represent smart contracts as state diagrams, emphasizing their states and transitions. An experiment was also carried out in order to show how state diagrams can facilitate the understanding of a contract. The results show that state diagrams do help understanding smart contracts.","PeriodicalId":423896,"journal":{"name":"2021 IEEE/ACS 18th International Conference on Computer Systems and Applications (AICCSA)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124607423","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