2020 International Conference on Computational Science and Computational Intelligence (CSCI)最新文献_第10页

Proposed Design for Effectively Expand Adaptive-ticks Feature in the Linux Kernel to Full Tickless Function 一种有效扩展Linux内核自适应滴答特性为完全无滴答功能的设计方案

2020 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2020-12-01 DOI: 10.1109/CSCI51800.2020.00233

Abdullah Aljuhni, Shaji Yusuf, C. Edward Chow, Oluwatobi Akanbi, Amer Aljaedi

{"title":"Proposed Design for Effectively Expand Adaptive-ticks Feature in the Linux Kernel to Full Tickless Function","authors":"Abdullah Aljuhni, Shaji Yusuf, C. Edward Chow, Oluwatobi Akanbi, Amer Aljaedi","doi":"10.1109/CSCI51800.2020.00233","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00233","url":null,"abstract":"Operating systems use timers available in the processor to trigger timer interrupts that help the kernel manage several tasks, ranging from time management of system software to scheduling and task switching. Traditionally, operating systems program the system timer to tick at regular intervals ranging from 1 ms to 100 ms, depending on the required system response and type of load. The periodic tick timer is the simplest and most common way of managing the system’s management activities. However, advancements in microprocessors and operating system scheduler allows for a more efficient and better-performing solution by eliminating regular timer interrupts. In this research, we illustrate the implementation of Adaptive-ticks feature and analyze the performance of Adaptive-ticks scheduler in Linux kernel version 4.0.9. We also propose a new full Adaptive-ticks design to support multiple tasks in the ready queue. This new proposed design extends the current Adaptive-ticks feature to improve performance and power efficiency by getting rid of unwanted interrupts.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134577666","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

Designing a composite platform for operational efficiency 设计一个提高作业效率的复合平台

2020 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2020-12-01 DOI: 10.1109/CSCI51800.2020.00263

Wangjie Xu, T. Fujimoto, Ziran Fan

引用次数: 0

Performance Analysis of Tor Website Fingerprinting over Time using Tree Ensemble Models 基于树集成模型的Tor网站指纹识别性能分析

2020 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2020-12-01 DOI: 10.1109/CSCI51800.2020.00097

Hyoung-Jin Oh, Donghoon Kim, Won-gyum Kim, Doosung Hwang

{"title":"Performance Analysis of Tor Website Fingerprinting over Time using Tree Ensemble Models","authors":"Hyoung-Jin Oh, Donghoon Kim, Won-gyum Kim, Doosung Hwang","doi":"10.1109/CSCI51800.2020.00097","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00097","url":null,"abstract":"Tor (The Onion Router) ensures network anonymity by encrypting contents through multiple relay nodes. Recent studies on website fingerprinting (WF) showed that websites can be identified with high accuracy by analyzing traffic data. However, websites are changing over time by updating contents, which can significantly reduce the accuracy of WF attacks. This study analyzes the performance over time by using ensemble models with excellent WF attack performance. The experiment are conducted in two cases with the initial model. The not updated analyzes the accuracy of models made from initial data over time, whereas the updated adds data that has changed over time to update the model to analyzes the accuracy. The average accuracy of the initial ensemble models is over 90.0% and the Rotation Forest algorithm shows high performance of 93.5%. Comparing the models trained after 30 days with the initial model, the classification performance dropped in both cases; the not updated dropped by more than 30.0% and the updated dropped by about 10.0%. The experimental results suggest that WF using machine learning may require model learning on a regular basis.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132201020","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: 3

Replay Spoof Attack Detection using Deep Neural Networks for Classification 基于深度神经网络分类的重放欺骗攻击检测

2020 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2020-12-01 DOI: 10.1109/CSCI51800.2020.00036

Salahaldeen Duraibi, Wasim Alhamdani, Frederick T. Sheldon

{"title":"Replay Spoof Attack Detection using Deep Neural Networks for Classification","authors":"Salahaldeen Duraibi, Wasim Alhamdani, Frederick T. Sheldon","doi":"10.1109/CSCI51800.2020.00036","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00036","url":null,"abstract":"In this paper, we explore the use of the deep learning approach for replay spoof detection in speaker verification systems. Automatic speaker verifications (ASVs) can be easily spoofed by previously recorded genuine speech. In order to counter the issues of spoofing, detecting spoofing attacks play an important role. Hence, we consider the detection of replay attack spoofing that is the most easily accomplished spoofing attack. In this light, we propose a deep neural network-based (DNN) classifier using a hybrid feature from Mel-frequency cepstral coefficient (MFCC) and constant Q cepstral coefficient (CQCC). Several experiments were conducted on the latest version of ASVspoof 2017 dataset. The results are compared with a base line system that uses the Gaussian mixture model (GMM) classifier with different features that include MFCC, CQCC, and the hybrid feature of the two. The experiment results reveal that the DNN classifier outperforms the conventional GMM classifier. It was found that the hybrid-based features are superior to single features, such as CQCC and MFCC in terms of equal error rate (ERR). In addition, like many previous researchers have found, it turned out that high-frequency regions of speech utterance convey much more discriminative information for replay attack detection.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121286175","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: 4

Behavior-based Outlier Detection for Indoor Environment 基于行为的室内环境异常点检测

2020 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2020-12-01 DOI: 10.1109/CSCI51800.2020.00135

Shinjin Kang, Sookyun Kim

引用次数: 0

Using Serde to Serialize and Deserialize DIS PDUs 使用Serde对DIS pdu进行序列化和反序列化

2020 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2020-12-01 DOI: 10.1109/CSCI51800.2020.00266

Noah W. Scott, D. Hodson, Richard Dill, M. Grimaila

{"title":"Using Serde to Serialize and Deserialize DIS PDUs","authors":"Noah W. Scott, D. Hodson, Richard Dill, M. Grimaila","doi":"10.1109/CSCI51800.2020.00266","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00266","url":null,"abstract":"Serialization is the process of translating a data structure into a format that can be stored and/or transmitted, and then subsequently reconstructed at a later time to create an identical clone of the original. The use of data serialization assures data objects can be transmitted, stored, and reliably reconstructed across differing computer architectures, even with different data type sizes or endianness, with no additional effort.Serializing the data in an architecture-independent format prevents the problems of byte ordering, memory layout, or representing data structures in different programming languages. This is especially important in the context of live, virtual, and constructive (LVC) simulation environments where multiple geographically separated computers, each with many independent threads, are connected and must communicate with as little latency as possible to remain near \"real-time\" like in terms of responsiveness.In this paper, we demonstrate the use of Serde, a Rust-based systems programming language crate, to serialize and deserialize IEEE standard Distribute Interactive Simulation (DIS) Protocol Data Units (PDUs) to support DIS-based network interoperability. The results show that Serde is an efficient mechanism for serialization/deserialization when using the inherently safe Rust programming language.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128972201","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

Density-Based Server Placement for Collaborative Virtual Services 基于密度的协同虚拟服务服务器布局

2020 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2020-12-01 DOI: 10.1109/CSCI51800.2020.00242

Sakir Yucel

引用次数: 1

Sensor-Based Air Pollution Prediction Using Deep CNN-LSTM 基于传感器的深度CNN-LSTM空气污染预测

2020 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2020-12-01 DOI: 10.1109/CSCI51800.2020.00127

Kabir Nagrecha, Pratyush Muthukumar, Emmanuel Cocom, Jeanne Holm, Dawn Comer, Irene Burga, M. Pourhomayoun

{"title":"Sensor-Based Air Pollution Prediction Using Deep CNN-LSTM","authors":"Kabir Nagrecha, Pratyush Muthukumar, Emmanuel Cocom, Jeanne Holm, Dawn Comer, Irene Burga, M. Pourhomayoun","doi":"10.1109/CSCI51800.2020.00127","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00127","url":null,"abstract":"The devastating impacts of air pollution have be-come more and more evident in recent years. As our measurement technologies improve, we gain better insight into the true impact of this deadly, yet often ignored, threat. The first step in reducing the damages caused by this problem is being able to analyze and predict its patterns. The problem of predicting air quality and the presence of particulate matter lies in the nature of the data needed to create an accurate system. The sheer number of factors affecting air quality mean that previously proposed approaches often utilize a great many sources of data, aiming to incorporate images, wind graphs, traffic information, and more. Yet in truth, most areas outside large metropolises lack ready access to high-quality data, preventing them from ever implementing an effective system. We propose a system utilizing a 1-D deep convolutional neural network to analyze past sensor readings and predict air pollutant concentrations up to a day in the future at a 3-hour resolution. We specifically developed this model for predicting PM2.5 values. The system receives PM2.5 sensor values and discovers temporal pattern in the data, which will be later used for prediction. By removing the dependency on complex data inputs, the system becomes accesible and easily implementable for any region. Despite this simplified approach, the results are comparable to — and often better than — any current state-of-the-art predictive systems in this domain.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115929970","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: 8

Unsupervised Learning with Word Embeddings Captures Quiescent Knowledge from COVID-19 Drugs Literature 使用词嵌入的无监督学习从COVID-19药物文献中捕获静态知识

2020 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2020-12-01 DOI: 10.1109/CSCI51800.2020.00081

Tasnim Gharaibeh, E. Doncker

引用次数: 1

Next Generation of Gallery Sharing in VR VR中的下一代画廊共享

2020 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2020-12-01 DOI: 10.1109/CSCI51800.2020.00313

W. Phillips, L. Deligiannidis

引用次数: 1