Proceedings of the 2021 ACM Southeast Conference最新文献

An empirical study of thermal attacks on edge platforms 边缘平台热攻击的实证研究

Proceedings of the 2021 ACM Southeast Conference Pub Date : 2021-04-15 DOI: 10.1145/3409334.3452071

Justin Duchatellier, Tyler Holmes, Kun Suo, Yong Shi

{"title":"An empirical study of thermal attacks on edge platforms","authors":"Justin Duchatellier, Tyler Holmes, Kun Suo, Yong Shi","doi":"10.1145/3409334.3452071","DOIUrl":"https://doi.org/10.1145/3409334.3452071","url":null,"abstract":"Cloud-edge systems are vulnerable to thermal attacks as the increased energy consumption may remain undetected, while occurring alongside normal, CPU-intensive applications. The purpose of our research is to study thermal effects on modern edge systems. We also analyze how performance is affected from the increased heat and identify preventative measures. We speculate that due to the technology being a recent innovation, research on cloud-edge devices and thermal attacks is scarce. Other research focuses on server systems rather than edge platforms. In our paper, we use a Raspberry Pi 4 and a CPU-intensive application to represent thermal attacks on cloud-edge systems. We performed several experiments with the Raspberry Pi 4 and used stress-ng, a benchmarking tool available on Linux distributions, to simulate the attacks. The resulting effects displayed drastic increases in the temperature and power consumption. The key impact of our research is to highlight the following risks and mitigation plans: the vulnerability of cloud-edge systems from thermal attacks, the capability for the attacks to go unnoticed, to further the understanding of edge devices as well as the prevention of these attacks.","PeriodicalId":148741,"journal":{"name":"Proceedings of the 2021 ACM Southeast Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129825514","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

Performance evaluation of a widely used implementation of the MQTT protocol with large payloads in normal operation and under a DoS attack 在正常操作和DoS攻击下，广泛使用的具有大有效负载的MQTT协议的性能评估

Proceedings of the 2021 ACM Southeast Conference Pub Date : 2021-04-15 DOI: 10.1145/3409334.3452067

Eric Gamess, Trent N. Ford, Monica A. Trifas

{"title":"Performance evaluation of a widely used implementation of the MQTT protocol with large payloads in normal operation and under a DoS attack","authors":"Eric Gamess, Trent N. Ford, Monica A. Trifas","doi":"10.1145/3409334.3452067","DOIUrl":"https://doi.org/10.1145/3409334.3452067","url":null,"abstract":"The Internet of Things (IoT) is the term coined to encompass the myriad of devices that have some data processing and transmitting capabilities. Due to the increasing number of IoT devices connected to the Internet, network protocols intended for IoT technology have gained interest. This paper analyzes the performance of one of the most popular ones, named MQTT (Message Queuing Telemetry Transport), focused on Mosquitto, a widely used implementation. Our principal metric is the transmission time, defined as the time it takes a message to pass from one client through the broker to another client, since MQTT uses a publish/subscribe model with a broker. We evaluate different scenarios against some base configurations to give a firm comparison on how different factors affect the performance of an MQTT system based on Mosquitto, for payload sizes ranging from 512 to 1,048,576 bytes. For example, we assess how different network technologies (Ethernet, WiFi in the 2.4 GHz and 5 GHz bands) and QoS levels may yield better results at different message payload sizes. We also make a broker software comparison, evaluating Mosquitto against ActiveMQ and RabbitMQ. Our experiments exhibited similar results, with a slight advantage for RabbitMQ. Finally, we provide measurements on how DoS attacks can affect the Mosquitto broker, by flooding it with illegal MQTT petitions or making a TCP SYN flood attack. The goal of this study is to help MQTT implementers in making adequate decisions when selecting the different hardware and software solutions, for their MQTT systems.","PeriodicalId":148741,"journal":{"name":"Proceedings of the 2021 ACM Southeast Conference","volume":"345 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116487413","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: 6

Item based recommendation using matrix-factorization-like embeddings from deep networks 基于项目的推荐，使用来自深度网络的类似矩阵分解的嵌入

Proceedings of the 2021 ACM Southeast Conference Pub Date : 2021-04-15 DOI: 10.1145/3409334.3452041

Vaidyanath Areyur Shanthakumar, Clark Barnett, Keith Warnick, P. A. Sudyanti, Vitalii Gerbuz, Tathagata Mukherjee

{"title":"Item based recommendation using matrix-factorization-like embeddings from deep networks","authors":"Vaidyanath Areyur Shanthakumar, Clark Barnett, Keith Warnick, P. A. Sudyanti, Vitalii Gerbuz, Tathagata Mukherjee","doi":"10.1145/3409334.3452041","DOIUrl":"https://doi.org/10.1145/3409334.3452041","url":null,"abstract":"In this paper we describe a method for computing item based recommendations using matrix-factorization-like embeddings of the items computed using a neural network. Matrix factorizations (MF) compute near optimal item embeddings by minimizing a loss that measures the discrepancy between the predicted and known values of a sparse user-item rating matrix. Though useful for recommendation tasks, they are computationally intensive and hard to compute for large sets of users and items. Hence there is need to compute MF-like embeddings using other less computationally intensive methods, which can be substituted for the actual ones. In this work we explore the possibility of doing the same using a deep neural network (DNN). Our network is trained to learn matrix-factorization-like embeddings from easy to compute natural language processing (NLP) based semantic embeddings. The resulting MF-like embeddings are used to compute recommendations using an anonymized user product engagement dataset from the online retail company Overstock.com. We present the results of using our embeddings for computing recommendations with the Overstock.com production dataset consisting of ~3.5 million items and ~6 million users. Recommendations from Overstock.com's own recommendation system is compared against those obtained by using our MF-like embeddings, by comparing the results from both to the ground truth, which in our case is actual user co-clicks data. Our results show that it is possible to use DNNs for efficiently computing MF-like embeddings which can then be used in conjunction with the NLP based embeddings to improve the recommendations obtained from the NLP based embeddings.","PeriodicalId":148741,"journal":{"name":"Proceedings of the 2021 ACM Southeast Conference","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124162117","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

A computer vision pipeline for automatic large-scale inventory tracking 一种用于大规模库存自动跟踪的计算机视觉管道

Proceedings of the 2021 ACM Southeast Conference Pub Date : 2021-04-15 DOI: 10.1145/3409334.3452063

Stephen Gregory, Utkarsh Singh, Jeffrey G. Gray, Jon Hobbs

{"title":"A computer vision pipeline for automatic large-scale inventory tracking","authors":"Stephen Gregory, Utkarsh Singh, Jeffrey G. Gray, Jon Hobbs","doi":"10.1145/3409334.3452063","DOIUrl":"https://doi.org/10.1145/3409334.3452063","url":null,"abstract":"Monitoring and tracking inventory is one of the most important aspects of administrating any large-scale enterprise operation that involves physical goods. One of the most evident examples of such operations is automotive manufacturing, especially for servicing a global customer base. We present a software solution of Intelligent Process Automation (IPA) that utilizes state-of-the-art computer vision (CV) and other algorithmic techniques to locate, detect, and manage inventory storage logistics using label information from simple warehouse images. When used in conjunction with a recently developed robotic imaging system, our pipeline can be shown to replace the need for costly, error-prone human input to the inventory tracking system. This paper outlines the technical and practical application of IPA fueled by deep learning. The specific motivation for this project was to address a critical need of Mercedes-Benz U.S. International (MBUSI), but the techniques could be applied more generally to other inventory management contexts. We also discuss how our pipeline produces an inexpensive, efficient, and generalizable solution that provides the capability to retrieve data from an unpredictable environment, in contrast to previous approaches.","PeriodicalId":148741,"journal":{"name":"Proceedings of the 2021 ACM Southeast Conference","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127921531","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

Wavelet transform-based feature extraction approach for epileptic seizure classification 基于小波变换的癫痫发作特征提取方法

Proceedings of the 2021 ACM Southeast Conference Pub Date : 2021-04-15 DOI: 10.1145/3409334.3452078

Md Khurram Monir Rabby, A. Islam, S. Belkasim, M. Bikdash

{"title":"Wavelet transform-based feature extraction approach for epileptic seizure classification","authors":"Md Khurram Monir Rabby, A. Islam, S. Belkasim, M. Bikdash","doi":"10.1145/3409334.3452078","DOIUrl":"https://doi.org/10.1145/3409334.3452078","url":null,"abstract":"In this research, a wavelet transform-based feature extraction approach is proposed for the detection of epileptic seizures from the EEG raw dataset. The proposed approach uses the Wavelet Transform (WT) method to divide the seizure and non-seizure classes of signals into multiple sub-bands and extracts the features of the dataset following Petrosian Fractal Dimension (PFD), Higuchi Fractal Dimension (HFD), and Singular Value Decomposition Entropy (SVDE). The Kruskal-Wallis test is performed to determine the difference in the random sampling and the extracted features are leveraged to divide the dataset into the training and testing sets for developing the model in order to train the network. The proposed approach is applied to the EEG dataset of Bonn University. Hence, the Neural Network (NN), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN) are used as preliminary models in the proposed approach for training the networks. As a preliminary analysis of the proposed approach, the training and testing Area Under the Curve (AUC) is calculated in the Receiver Operating Characteristic (ROC) curve to measure the performances of the existing models. The primary results show that, in the proposed approach, the performance of ANN is better than NN, SVM, and CNN.","PeriodicalId":148741,"journal":{"name":"Proceedings of the 2021 ACM Southeast Conference","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128493138","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

A study of state-of-the-art energy saving on edges 最先进的边缘节能研究

Proceedings of the 2021 ACM Southeast Conference Pub Date : 2021-04-15 DOI: 10.1145/3409334.3452079

Kousalya Banka, Kun Suo, Yong Shi, S. Baidya

引用次数: 1

Benefits of combining dimensional attention and working memory for partially observable reinforcement learning problems 结合维度注意和工作记忆对部分可观察强化学习问题的好处

Proceedings of the 2021 ACM Southeast Conference Pub Date : 2021-04-15 DOI: 10.1145/3409334.3452072

Ngozi Omatu, Joshua L. Phillips

{"title":"Benefits of combining dimensional attention and working memory for partially observable reinforcement learning problems","authors":"Ngozi Omatu, Joshua L. Phillips","doi":"10.1145/3409334.3452072","DOIUrl":"https://doi.org/10.1145/3409334.3452072","url":null,"abstract":"Neuroscience provides a rich source of inspiration for new types of algorithms and architectures to employ when building AI and the resulting biologically-plausible approaches that provide formal, testable models of brain function. The working memory toolkit (WMtk), was developed to assist the integration of an artificial neural network (ANN)-based computational neuroscience model of working memory into reinforcement learning (RL) agents, mitigating the details of ANN design and providing a simple symbolic encoding interface. While the WMtk allows RL agents to perform well in partially-observable domains, it requires prefiltering of sensory information by the programmer: a task often delegated to dimensional attention mechanisms in other cognitive architectures. To fill this gap, we develop and test a biologically-plausible dimensional attention filter for the WMtk and validate model performance using a partially-observable 1D maze task. We show that the attention filter improves learning behavior in two ways by: 1) speeding up learning in the short-term, early in training and 2) developing emergent alternative strategies which optimize performance over the long-term.","PeriodicalId":148741,"journal":{"name":"Proceedings of the 2021 ACM Southeast Conference","volume":"346 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116532768","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

Modification and complexity analysis of an incremental learning algorithm under the VPRS model VPRS模型下一种增量学习算法的改进及复杂度分析

Proceedings of the 2021 ACM Southeast Conference Pub Date : 2021-04-15 DOI: 10.1145/3409334.3452076

Xuguang Chen

引用次数: 1

Sentiment analysis on COVID tweets using COVID-Twitter-BERT with auxiliary sentence approach 使用COVID- twitter - bert辅助句方法对COVID推文进行情感分析

Proceedings of the 2021 ACM Southeast Conference Pub Date : 2021-04-15 DOI: 10.1145/3409334.3452074

Hung-Yeh Lin, Teng-Sheng Moh

{"title":"Sentiment analysis on COVID tweets using COVID-Twitter-BERT with auxiliary sentence approach","authors":"Hung-Yeh Lin, Teng-Sheng Moh","doi":"10.1145/3409334.3452074","DOIUrl":"https://doi.org/10.1145/3409334.3452074","url":null,"abstract":"Sentiment analysis is a fascinating area as a natural language understanding benchmark to evaluate customers' feedback and needs. Moreover, sentiment analysis can be applied to understand the people's reactions to public events such as the presidential elections and disease pandemics. Recent works in sentiment analysis on COVID-19 present a domain-targeted Bidirectional Encoder Representations from Transformer (BERT) language model, COVID-Twitter BERT (CT-BERT). However, there is little improvement in text classification using a BERT-based language model directly. Therefore, an auxiliary approach using BERT was proposed. This method converts single-sentence classification into pair-sentence classification, which solves the performance issue of BERT in text classification tasks. In this paper, we combine a pre-trained BERT model from COVID-related tweets and the auxiliary-sentence method to achieve better classification performance on COVID tweets sentiment analysis. We show that converting single-sentence classification into pair-sentence classification extends the dataset and obtains higher accuracies and F1 scores. However, we expect a domain-specific language model would perform better than a general language model. In our results, we show that the performance of CT-BERT does not necessarily outperform BERT specifically in understanding sentiments.","PeriodicalId":148741,"journal":{"name":"Proceedings of the 2021 ACM Southeast Conference","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125301135","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: 6

Fast streaming translation using machine learning with transformer 快速流翻译使用机器学习与变压器

Proceedings of the 2021 ACM Southeast Conference Pub Date : 2021-04-15 DOI: 10.1145/3409334.3452059

Jiabao Qiu, M. Moh, Teng-Sheng Moh

引用次数: 1