2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)最新文献_第6页

A Review of Dark Web: Trends and Future Directions 暗网综述:趋势和未来方向

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00283

Shahriar Sobhan, Timothy Williams, Md Jobair Hossain Faruk, Juan Rodriguez, Masrura Tasnim, Edwin Mathew, Jack Wright, H. Shahriar

{"title":"A Review of Dark Web: Trends and Future Directions","authors":"Shahriar Sobhan, Timothy Williams, Md Jobair Hossain Faruk, Juan Rodriguez, Masrura Tasnim, Edwin Mathew, Jack Wright, H. Shahriar","doi":"10.1109/COMPSAC54236.2022.00283","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00283","url":null,"abstract":"The dark web is often discussed in taboo by many who are unfamiliar with the subject. However, this paper takes a dive into the skeleton of what constructs the dark web by compiling the research of published essays. The Onion Router (TOR) and other discussed browsers are specialized web browsers that provide anonymity by going through multiple servers and encrypted networks between the host and client, hiding the IP address of both ends. This provides difficulty in terms of controlling or monitoring the dark web, leading to its popularity in criminal underworlds. In this work, we provide an overview of data mining and penetration testing tools that are being widely used to crawl and collect data. We compare the tools to provide strengths and weaknesses of the tools while providing challenges of harnessing massive data from dark web using crawlers and penetration testing tools including machine learning (ML) techniques. Despite the effort to crawl dark web has progressed, there are still rooms to advance existing approaches to combat the ever-changing landscape of the dark web.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124906021","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

A Cloud-Native Online Judge System 云原生在线裁判系统

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00204

Guan-Chen Pan, Pangfeng Liu, Jan-Jan Wu

{"title":"A Cloud-Native Online Judge System","authors":"Guan-Chen Pan, Pangfeng Liu, Jan-Jan Wu","doi":"10.1109/COMPSAC54236.2022.00204","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00204","url":null,"abstract":"Online Judge Systems are designed for the reliable evaluation of source code submitted by users. The system queues the code, compiles and tests them typically in a FIFO manner. However, we found that the cloud-native design of an online judge system is still a pending issue. Many existing open-source online judge systems are hard to deploy and scale, due to the tight coupling with some specific environments and the vague boundaries in their system architectures. In addition, these online judge systems hardly provide the support of resource scheduling as they are usually more concerned about the homogeneity of the resources. In this research, we design and develop a cloud-native online judge system that is able to (1) be built and run stably in dynamic environments (2) scale vertically and horizontally to the workload (3) do resource scheduling over CPUs and GPUs. Furthermore, this research also analyzes the consequence of adopting some modernly advocated design approaches and technologies; these are: microservice architecture design, event-driven architecture, domain-driven design, and containerization technologies such as Docker and Kubernetes.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122895874","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

The development of ethically informed standards for intelligent monitoring systems of electric machines 电机智能监控系统伦理信息标准的制定

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00254

Kun Shang, Stuart McDonald, G. Buticchi, V. Brusic

引用次数: 2

SIM-PIPE DryRunner: An approach for testing container-based big data pipelines and generating simulation data SIM-PIPE DryRunner:一种测试基于容器的大数据管道和生成模拟数据的方法

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00182

Aleena Thomas, Nikolay Nikolov, Antoine Pultier, D. Roman, B. Elvesæter, A. Soylu

引用次数: 1

Deep Learning Predictions for Cryptocurrencies 加密货币的深度学习预测

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00202

A. Thavaneswaran, You Liang, Sulalitha Bowala, Alex Paseka, M. Ghahramani

{"title":"Deep Learning Predictions for Cryptocurrencies","authors":"A. Thavaneswaran, You Liang, Sulalitha Bowala, Alex Paseka, M. Ghahramani","doi":"10.1109/COMPSAC54236.2022.00202","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00202","url":null,"abstract":"Recently there has been a growing interest in applying neural network modelling from natural language processing to financial time series prediction problems in computational finance. Cryptocurrency price prediction is a challenging problem with non-stationary market price and volatility clustering. Cryp-tocurrency data tends to be non-stationary, which means that predictive information extracted using deep learning techniques on observed data can not be used with future data. Moreover, there is a very little signal in cryptocurrency data to indicate the future direction of the market. This paper proposes a sensible way to frame the prediction problem as a dynamic regression problem by defining the features in the feedforward neural networks and the target as an appropriate average of the historical data. The novelty of this paper is to use deep learning algorithms and statistical bootstrapping to obtain cryptocurrency price prediction and the corresponding prediction intervals. It is shown that neural networks are capable of modelling nonlinearity directly for nonlinear time series models. The proposed hybrid approach is evaluated using simulated and cryptocurrency data through numerical experiments. Moreover, Gaussian and boot-strap prediction intervals for the price and the volatility of the prediction errors, are also discussed in some detail.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128124039","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

Unsupervised Markdown Feature-Aware Keywords Extraction Towards Technology Blogs 面向科技博客的无监督降价特征感知关键词提取

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00039

Yangyang Wang, Liping Hua, Hui Zhao, Lingfeng Yang

{"title":"Unsupervised Markdown Feature-Aware Keywords Extraction Towards Technology Blogs","authors":"Yangyang Wang, Liping Hua, Hui Zhao, Lingfeng Yang","doi":"10.1109/COMPSAC54236.2022.00039","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00039","url":null,"abstract":"A vast amount of blogs are generated from online technology communities every day. Most of them are in Markdown format. The increase of Markdown documents has brought opportunities and challenges to many natural language processing tasks. Extracting keywords from technology blogs is of great value for discovering, retrieving, and sharing knowl-edge about technical blogs. The mainstream keyword extraction algorithms remain to use statistical char-acteristics of words to determine the keywords of a document, seldom considering the structure char-acteristics of the document that potentially express the semantic information. We argue that Markdown markup features as well as the textual content of the document are both concerned with the keywords extraction. In this paper, we propose a novel un-supervised Markdown markup features aware key-words extraction algorithm for technology blogs. The algorithm integrates Markdown markup syntax in-formation with a blog text representation. Through experiments against TF-IDF, TextRank, and PositionRank algorithms on a real Markdown document dataset, our algorithm achieves higher performance with a substantial improvement when the number of keywords extracted is greater than 3.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132525477","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

Towards a Commercial Pipeline for Field Crop Identification 迈向商业化的田间作物鉴定管道

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00098

Dafina Jeaca, Nicu-Cosmin Ursache, Andrei Panu

引用次数: 0

Using Metamorphic Relation Violation Regions to Support a Simulation Framework for the Process of Metamorphic Testing 利用变质关系违背区域支持变质试验过程的仿真框架

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00274

Zhihao Ying, A. Bellotti, D. Towey, T. Chen, Z. Zhou

{"title":"Using Metamorphic Relation Violation Regions to Support a Simulation Framework for the Process of Metamorphic Testing","authors":"Zhihao Ying, A. Bellotti, D. Towey, T. Chen, Z. Zhou","doi":"10.1109/COMPSAC54236.2022.00274","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00274","url":null,"abstract":"Metamorphic testing (MT) has been growing in pop-ularity, but it can still be quite challenging and time-consuming to assess its performance. Typical approaches to performance assessment can require a series of steps, and depend on a variety of factors, often requiring serendipity. This can be a bottleneck for some aspects of MT research. Central to MT, metamorphic relations (MRs) represent necessary properties of the system under test (SUT). In traditional software testing, simulations are often employed to examine and compare the performance of dif-ferent testing strategies. However, these simulations are typically designed based on the assumed availability (and applicability) of a test oracle - a mechanism to decide the correctness of the SUT output or behaviour. A key reason for the popularity of MT is its proven record of effective software testing, without the need for a test oracle. This strength, however, also means that traditional ways of using simulations to analyse software testing approaches are not applicable for MT. This lack of cheap and fast ways to conduct simulation analyses of MT is a hurdle for many aspects of MT research, and may be an obstacle to its more widespread adoption. To address this, in this paper we introduce the concept of MR-violation regions (MRVRs), and show how they can be used for a certain category of MRs, Deterministic MRs (DMRs), to build simulation tools for MT. We analyse the differences between MRVRs and traditional, oracle-defined failure regions; and report on a preliminary case study exploring MRVRs in numerical-input-domain systems from previous MT studies. We anticipate that the proposed MT simulation framework may facilitate more research into MT, and may help lead to its more widespread adoption.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133265996","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

Towards a Multi-Tenant Microservice Architecture: An Industrial Experience 迈向多租户微服务架构:一种工业体验

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00100

César Batista, B. Proença, Everton Cavalcante, T. Batista, F. Morais, Henrique Medeiros

引用次数: 0

Chinese Medical Event Extraction Based on Hybrid Neural Network 基于混合神经网络的中医事件提取

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00225

Liyin Yang, Jianqiang Li, Zhichao Zhu, Xiangmin Dong, F. Akhtar

{"title":"Chinese Medical Event Extraction Based on Hybrid Neural Network","authors":"Liyin Yang, Jianqiang Li, Zhichao Zhu, Xiangmin Dong, F. Akhtar","doi":"10.1109/COMPSAC54236.2022.00225","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00225","url":null,"abstract":"The medical record system is becoming more and more irreplaceable in the medical industry, and the electronic medical record data continues to grow over time. There is a lot of knowledge and information in these accumulated medical resources that can be used for medical services, but how to obtain this valuable medical information is a difficult problem that needs to be overcome. Event extraction belongs to information extraction technology, which is an effective solution that can automatically mine knowledge and information from text data. Many studies have applied it to the text data of electronic medical records to extract medical events related to medical treatment. They have achieved certain results for medical services. However, these studies usually lack the synergistic consideration of global features and local features of medical text information in terms of Chinese medical record text mining and utilization. To better solve this problem, we try to propose a hybrid neural network model (BCBC) based on CNN-BILSTM-CRF. By integrating CNN and BILSTM, the local and global features of the text are comprehensively extracted, which makes up for the insufficient semantic capture of a single model in the traditional method. Through experimental verification, the hybrid neural network model BCBC proposed in this paper outperforms other previous advanced methods in event extraction and can efficiently complete the event extraction task.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133693382","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