2022 Intermountain Engineering, Technology and Computing (IETC)最新文献_第8页

Exploring AV1 Encoder Potentials for Priority-Driven Wireless Multimedia Services 探索AV1编码器在优先级驱动的无线多媒体服务中的潜力

2022 Intermountain Engineering, Technology and Computing (IETC) Pub Date : 2022-05-01 DOI: 10.1109/ietc54973.2022.9796903

Evan Ballesteros, K. Ramamoorthy, Wei Wang

{"title":"Exploring AV1 Encoder Potentials for Priority-Driven Wireless Multimedia Services","authors":"Evan Ballesteros, K. Ramamoorthy, Wei Wang","doi":"10.1109/ietc54973.2022.9796903","DOIUrl":"https://doi.org/10.1109/ietc54973.2022.9796903","url":null,"abstract":"World wide internet data usage is growing at a rapid rate and one of the main reasons for this high data usage is the increasing use of media streaming services. According to Cisco, video streaming will take up 82% of the world’s internet traffic by this year, 2022. Media streaming services use a large amount of data due to videos being inherently such large files which, for streaming services, need to be transferred over the internet. For this reason, we are researching methods to impact the use of a new generation open source video encoder, AV1, with wireless communications. Though the encoder is already more efficient at compressing video files than previous encoders, our goal was to research and understand AV1 in order to customize wireless protocols that optimize its usage in streaming scenarios. In our first attempt at understanding this encoder, we went through processes of tweaking settings built into the encoder in order to find optimal video quality when compared to file size. The file size is an important metric that correlates with how much data would have to be streamed. After this we went on to investigate how we could possibly estimate the per frame quality contribution of each frame transmitted. This goal was inspired by that of previous H.264/H.265 encoders’ I, P, and B frames, which each have their own distinguishable amount of quality contribution that can be split between 3 levels: high, medium, and low. We believe that the method provided could have a good indication of quality contribution similar to that of I, P and B frames with high, medium, or low quality contribution.","PeriodicalId":251518,"journal":{"name":"2022 Intermountain Engineering, Technology and Computing (IETC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124701999","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

John the Ripper: An Examination and Analysis of the Popular Hash Cracking Algorithm 开膛手约翰:流行哈希破解算法的检验与分析

2022 Intermountain Engineering, Technology and Computing (IETC) Pub Date : 2022-05-01 DOI: 10.1109/ietc54973.2022.9796671

Kaden Marchetti, P. Bodily

引用次数: 1

Learn Dynamic Facial Motion Representations Using Transformer Encoder 学习动态面部运动表示使用变压器编码器

2022 Intermountain Engineering, Technology and Computing (IETC) Pub Date : 2022-05-01 DOI: 10.1109/ietc54973.2022.9796917

Zheng Sun, Andrew W. Sumsion, Shad A. Torrie, Dah-Jye Lee

{"title":"Learn Dynamic Facial Motion Representations Using Transformer Encoder","authors":"Zheng Sun, Andrew W. Sumsion, Shad A. Torrie, Dah-Jye Lee","doi":"10.1109/ietc54973.2022.9796917","DOIUrl":"https://doi.org/10.1109/ietc54973.2022.9796917","url":null,"abstract":"Human face analysis is an essential topic in visual computing. Many of our daily applications, such as face-priority auto focus in camera, face-based identity verification, and TikTok stickers, are unattainable without face analysis techniques. In the past ten years, face-related visual computing tasks like face detection, face recognition, and facial expression classification have improved drastically in performance, benefiting from the rapid development of deep learning theory. This work explores how to model dynamic facial motion using a learning-based method. Our proposed model takes video clips containing customized facial motion as input and generates a uni-size vector (the embedding) as the output. We have inspected two different encoders–recurrent neural networks and transformers to extract the temporal features from the video clip. We collected our own facial motion analysis dataset because there is no suitable datasets for our facial motion analysis task. Although our domain-specific dataset is small compared to the well-known public datasets for ordinary face-related tasks, we adopt a transfer learning approach, and a data augmentation method (random trimming) to help the model converge. The experimental results show that the transformer-based encoder performs better than the RNN baseline, and the best F1-score with our validation data is 0.889.","PeriodicalId":251518,"journal":{"name":"2022 Intermountain Engineering, Technology and Computing (IETC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115468532","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

Audio Event Recognition in Noisy Environments using Power Spectral Density and Dimensionality Reduction 基于功率谱密度和降维的噪声环境下音频事件识别

2022 Intermountain Engineering, Technology and Computing (IETC) Pub Date : 2022-05-01 DOI: 10.1109/ietc54973.2022.9796710

Md Siddat Bin Nesar, Bradley M. Whitaker

引用次数: 0

Adaptive Encrypted Traffic Characterization via Deep Representation Learning 基于深度表示学习的自适应加密流量表征

2022 Intermountain Engineering, Technology and Computing (IETC) Pub Date : 2022-05-01 DOI: 10.1109/ietc54973.2022.9796734

Jonathan Wintrode, D. Detienne

{"title":"Adaptive Encrypted Traffic Characterization via Deep Representation Learning","authors":"Jonathan Wintrode, D. Detienne","doi":"10.1109/ietc54973.2022.9796734","DOIUrl":"https://doi.org/10.1109/ietc54973.2022.9796734","url":null,"abstract":"Near ubiquitous encryption poses a challenge for security and quality of service (QoS) applications that rely on deep packet inspection (DPI) techniques for categorizing traffic types or threats. However, recent work has shown that machine learning (ML) on temporal flow statistics as well as convolutional neural network (CNN) methods applied to raw packets are able to classify network traffic even in the face of encryption. Unfortunately, many such methods often lack the ability to generalize to new categories, a critical requirement in our constantly evolving networks. Building on previous approaches we apply CNN models to the characterization task within a deep representation learning framework. The network acts as a feature extractor which is input to a lightweight support vector machine (SVM) classifier for the final output. By training the networks with an angular softmax loss in addition to the typical crossentropy loss, we can improve on the state of the art in terms of both classification accuracy and detection error. Furthermore we demonstrate that learned features provide the ability to label traffic categories not seen in neural network training.","PeriodicalId":251518,"journal":{"name":"2022 Intermountain Engineering, Technology and Computing (IETC)","volume":"2009 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125983852","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

Driver Compliance at All-Way Stop-Controlled Intersections 全程停车控制十字路口的驾驶员依从性

2022 Intermountain Engineering, Technology and Computing (IETC) Pub Date : 2022-05-01 DOI: 10.1109/ietc54973.2022.9796668

Khaled Shaaban, Steven T. Taylor, R. Jackson, Dustin Wall

引用次数: 0

Offline Signature Verification: A Study on Total Variation versus CNN 离线签名验证:全变分与CNN的对比研究

2022 Intermountain Engineering, Technology and Computing (IETC) Pub Date : 2022-05-01 DOI: 10.1109/ietc54973.2022.9796924

Kateryna Anatska, Mohammad Shekaramiz

引用次数: 0

Review of Factors Affecting Public Transportation Ridership 影响公共交通乘客的因素综述

2022 Intermountain Engineering, Technology and Computing (IETC) Pub Date : 2022-05-01 DOI: 10.1109/ietc54973.2022.9796772

Khaled Shaaban, A. Siam

引用次数: 0

KAMI: Leveraging the power of crowd-sourcing to solve complex, real-world problems KAMI:利用众包的力量来解决复杂的现实问题

2022 Intermountain Engineering, Technology and Computing (IETC) Pub Date : 2022-05-01 DOI: 10.1109/ietc54973.2022.9796945

Kaden Marchetti, P. Bodily

引用次数: 1

Nonlinear Control Algorithm for Systems with Convex Polytope Bounded Nonlinearities 凸多边形有界非线性系统的非线性控制算法

2022 Intermountain Engineering, Technology and Computing (IETC) Pub Date : 2022-05-01 DOI: 10.1109/ietc54973.2022.9796775

Olli Jansson, Matt Harris

引用次数: 0