This paper describes an innovative Open Information Extraction method known as ATP-OIE1. It utilizes extraction patterns to find semantic relations. These patterns are generated automatically from examples, so it has greater autonomy than methods based on fixed rules. ATP-OIE can also summon other methods, ReVerb and ClausIE, if it is unable to find valid semantic relations in a sentence, thus improving its recall. In these cases, it is capable of generating new extraction patterns online, which improves its autonomy. It also implements different mechanisms to prevent common errors in the extraction of semantic relations. Lastly, ATP-OIE was compared with other state-of-the-art methods in a well known texts database: Reuters-21578, obtaining a higher precision than with other methods.
{"title":"ATP-OIE: An Autonomous Open Information Extraction Method","authors":"J. M. Rodríguez, H. Merlino, Patricia Pesado","doi":"10.1145/3388142.3388166","DOIUrl":"https://doi.org/10.1145/3388142.3388166","url":null,"abstract":"This paper describes an innovative Open Information Extraction method known as ATP-OIE1. It utilizes extraction patterns to find semantic relations. These patterns are generated automatically from examples, so it has greater autonomy than methods based on fixed rules. ATP-OIE can also summon other methods, ReVerb and ClausIE, if it is unable to find valid semantic relations in a sentence, thus improving its recall. In these cases, it is capable of generating new extraction patterns online, which improves its autonomy. It also implements different mechanisms to prevent common errors in the extraction of semantic relations. Lastly, ATP-OIE was compared with other state-of-the-art methods in a well known texts database: Reuters-21578, obtaining a higher precision than with other methods.","PeriodicalId":409298,"journal":{"name":"Proceedings of the 2020 the 4th International Conference on Compute and Data Analysis","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117258837","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}
Akalanka Mailewa Dissanayaka, S. Mengel, L. Gittner, H. Khan
A Vulnerability Management system is a disciplined, programmatic approach to discover and mitigate vulnerabilities in a system. While securing systems from data exploitation and theft, Vulnerability Management works as a cyclical practice of identifying, assessing, prioritizing, remediating, and mitigating security weaknesses. In this approach, root cause analysis is conducted to find solutions for the problematic areas in policy, process, and standards including configuration standards. Three major reasons make Vulnerability Assessment and Management a vital part in IT risk management. The reasons are, namely, 1. Persistent Threats - Attacks exploiting security vulnerabilities for financial gain and criminal agendas continue to dominate headlines, 2. Regulations - Many government and industry regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) and Sarbanes-Oxley (SOX), mandate rigorous vulnerability management practices, and 3. Risk Management - Mature organizations treat vulnerability assessment and management as a key risk management component [1]. Thus, as opposed to a reactive and technology-oriented approach, a well-organized and executed Vulnerability Management system is proactive and business-oriented. This research initially collects all the vulnerabilities associated with the Data Analytic Framework Implemented with MongoDB on Linux Containers (LXCs) by using the vulnerability analysis testbed with seven deferent analyzing tools. Thereafter, this research work first prioritizes all the vulnerabilities using "Low", "Medium", and "High" according to their severity level. Then, it discovers and analyzes the root cause of fifteen various vulnerabilities with different severities. Finally, according to each of the vulnerability root causes, this research proposes security techniques, to avoid or mitigate those vulnerabilities from the current system.
漏洞管理系统是一种规范的、程序化的方法，用于发现和减轻系统中的漏洞。在保护系统免受数据利用和盗窃的同时，漏洞管理作为识别、评估、确定优先级、修复和减轻安全弱点的周期性实践。在这种方法中，进行根本原因分析，以找到策略、流程和标准(包括配置标准)中有问题区域的解决方案。主要有三个原因使得脆弱性评估和管理成为IT风险管理的重要组成部分。原因是:1。持续的威胁-利用安全漏洞获取经济利益和犯罪议程的攻击继续占据头条新闻。法规—许多政府和行业法规，如《健康保险可携带性和责任法案》(HIPAA)和《萨班斯-奥克斯利法案》(SOX)，要求严格的漏洞管理实践;风险管理——成熟的组织将脆弱性评估和管理视为风险管理的关键组成部分[1]。因此，与被动的和面向技术的方法相反，组织良好并执行良好的漏洞管理系统是主动的和面向业务的。本研究通过使用包含七种不同分析工具的漏洞分析测试平台，初步收集了与MongoDB on Linux Containers (LXCs)相关的所有漏洞。随后，本研究工作首先根据漏洞的严重程度，用“低”、“中”、“高”对所有漏洞进行优先级排序。然后，发现并分析了15个不同严重程度的漏洞的根本原因。最后，根据每个漏洞的根源，本研究提出了安全技术，以避免或减轻这些漏洞来自当前系统。
{"title":"Vulnerability Prioritization, Root Cause Analysis, and Mitigation of Secure Data Analytic Framework Implemented with MongoDB on Singularity Linux Containers","authors":"Akalanka Mailewa Dissanayaka, S. Mengel, L. Gittner, H. Khan","doi":"10.1145/3388142.3388168","DOIUrl":"https://doi.org/10.1145/3388142.3388168","url":null,"abstract":"A Vulnerability Management system is a disciplined, programmatic approach to discover and mitigate vulnerabilities in a system. While securing systems from data exploitation and theft, Vulnerability Management works as a cyclical practice of identifying, assessing, prioritizing, remediating, and mitigating security weaknesses. In this approach, root cause analysis is conducted to find solutions for the problematic areas in policy, process, and standards including configuration standards. Three major reasons make Vulnerability Assessment and Management a vital part in IT risk management. The reasons are, namely, 1. Persistent Threats - Attacks exploiting security vulnerabilities for financial gain and criminal agendas continue to dominate headlines, 2. Regulations - Many government and industry regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) and Sarbanes-Oxley (SOX), mandate rigorous vulnerability management practices, and 3. Risk Management - Mature organizations treat vulnerability assessment and management as a key risk management component [1]. Thus, as opposed to a reactive and technology-oriented approach, a well-organized and executed Vulnerability Management system is proactive and business-oriented. This research initially collects all the vulnerabilities associated with the Data Analytic Framework Implemented with MongoDB on Linux Containers (LXCs) by using the vulnerability analysis testbed with seven deferent analyzing tools. Thereafter, this research work first prioritizes all the vulnerabilities using \"Low\", \"Medium\", and \"High\" according to their severity level. Then, it discovers and analyzes the root cause of fifteen various vulnerabilities with different severities. Finally, according to each of the vulnerability root causes, this research proposes security techniques, to avoid or mitigate those vulnerabilities from the current system.","PeriodicalId":409298,"journal":{"name":"Proceedings of the 2020 the 4th International Conference on Compute and Data Analysis","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130840930","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}
In workplaces, there is a massive amount of unstructured data from different sources. In this paper, we present a case study that explains how can through communications between employees, we can help to prioritize tasks requests to increase the efficiency of their works for both technical and non-technical workers. This involves managing daily incoming tasks based on their level of urgency and importance.To allow all workers to utilize the urgency-importance matrix as a time-management tool, we need to automate this tool. The textual content of incoming tasks are analyzed, and metrics related to urgency and importance are extracted. A third factor (i.e., the response variable) is defined based on the two input variables (urgency and importance). Then, machine learning applied to the data to predict the class of incoming tasks based on data outcome desired. We used ordinal regression, neural networks, and decision tree algorithms to predict the four levels of task priority. We measure the performance of all using recalls, precisions, and F-scores. All classifiers perform higher than 89% in terms of all measures.
{"title":"Text mining for incoming tasks based on the urgency/importance factors and task classification using machine learning tools","authors":"Y. Alshehri","doi":"10.1145/3388142.3388153","DOIUrl":"https://doi.org/10.1145/3388142.3388153","url":null,"abstract":"In workplaces, there is a massive amount of unstructured data from different sources. In this paper, we present a case study that explains how can through communications between employees, we can help to prioritize tasks requests to increase the efficiency of their works for both technical and non-technical workers. This involves managing daily incoming tasks based on their level of urgency and importance.To allow all workers to utilize the urgency-importance matrix as a time-management tool, we need to automate this tool. The textual content of incoming tasks are analyzed, and metrics related to urgency and importance are extracted. A third factor (i.e., the response variable) is defined based on the two input variables (urgency and importance). Then, machine learning applied to the data to predict the class of incoming tasks based on data outcome desired. We used ordinal regression, neural networks, and decision tree algorithms to predict the four levels of task priority. We measure the performance of all using recalls, precisions, and F-scores. All classifiers perform higher than 89% in terms of all measures.","PeriodicalId":409298,"journal":{"name":"Proceedings of the 2020 the 4th International Conference on Compute and Data Analysis","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130647346","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}
Words selection, writing style, stories cherry-picking, and many other factors play a role in framing news articles to fit the targeted audience or to align with the authors' beliefs. Hence, reporting facts alone is not evidence of bias-free journalism. Since the 2016 United States presidential elections, researchers focused on the media influence on the results of the elections. The news media attention has deviated from political parties to candidates. The news media shapes public perception of political candidates through news personalization. Despite its criticality, we are not aware of any studies which have examined news personalization from the machine learning or deep neural network perspective. In addition, some candidates accuse the media of favoritism which jeopardizes their chances of winning elections. Multiple methods were introduced to place news sources on one side of the political spectrum or the other, yet the mainstream media claims to be unbiased. Therefore, to avoid inaccurate assumptions, only news sources that have stated clearly their political affiliation are included in this research. In this paper, we constructed two datasets out of news articles written about the last two U.S. presidents with respect to news websites' political affiliation. Multiple intelligent models were developed to automatically predict the political affiliation of the personalized unseen article. The main objective of these models is to detect the political ideology of personalized news articles. Although the newly constructed datasets are highly imbalanced, the performance of the intelligent models is reasonably good. The results of the intelligent models are reported with a comparative analysis.
{"title":"Ideology Detection of Personalized Political News Coverage: A New Dataset","authors":"Khudran Alzhrani","doi":"10.1145/3388142.3388149","DOIUrl":"https://doi.org/10.1145/3388142.3388149","url":null,"abstract":"Words selection, writing style, stories cherry-picking, and many other factors play a role in framing news articles to fit the targeted audience or to align with the authors' beliefs. Hence, reporting facts alone is not evidence of bias-free journalism. Since the 2016 United States presidential elections, researchers focused on the media influence on the results of the elections. The news media attention has deviated from political parties to candidates. The news media shapes public perception of political candidates through news personalization. Despite its criticality, we are not aware of any studies which have examined news personalization from the machine learning or deep neural network perspective. In addition, some candidates accuse the media of favoritism which jeopardizes their chances of winning elections. Multiple methods were introduced to place news sources on one side of the political spectrum or the other, yet the mainstream media claims to be unbiased. Therefore, to avoid inaccurate assumptions, only news sources that have stated clearly their political affiliation are included in this research. In this paper, we constructed two datasets out of news articles written about the last two U.S. presidents with respect to news websites' political affiliation. Multiple intelligent models were developed to automatically predict the political affiliation of the personalized unseen article. The main objective of these models is to detect the political ideology of personalized news articles. Although the newly constructed datasets are highly imbalanced, the performance of the intelligent models is reasonably good. The results of the intelligent models are reported with a comparative analysis.","PeriodicalId":409298,"journal":{"name":"Proceedings of the 2020 the 4th International Conference on Compute and Data Analysis","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130914446","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}
In the offshore oil and gas industry, petroleum in each well of a remote wellhead platform (WHP) is extracted naturally from the ground to the sales delivery point. However, when the oil pressure drops or the well is nearly depleted, the flow rate up to the WHP declines. Installing a Wellhead Compressor (WC) on the WHP is the solution [9]. The WC acts locally on the selected wells and reduces back pressure, thereby substantially enhancing the efficiency of oil and gas recovery [21]. The WC sensors transmit data back to the historian time series database, and intelligent alarm systems are utilized as a critical tool to minimize unscheduled downtime which adversely affects production reliability, as well as monitoring time and cost burden of operating engineers. In this paper, an Attention-Based Bidirectional Long Short-Term Memory (ABD-LSTM) model is presented for WC failure prediction. We also propose feature extraction and data reduction techniques as complementary methods to improve the effectiveness of the training process in a large-scale dataset. We evaluate our model performance based on real WC sensor data. Compared to other Machine Learning (ML) algorithms, our proposed methodology is more powerful and accurate. Our proposed ABD-LSTM achieved an optimal F1 score of 85.28%.
{"title":"Wellhead Compressor Failure Prediction Using Attention-based Bidirectional LSTMs with Data Reduction Techniques","authors":"Wirasak Chomphu, B. Kijsirikul","doi":"10.1145/3388142.3388154","DOIUrl":"https://doi.org/10.1145/3388142.3388154","url":null,"abstract":"In the offshore oil and gas industry, petroleum in each well of a remote wellhead platform (WHP) is extracted naturally from the ground to the sales delivery point. However, when the oil pressure drops or the well is nearly depleted, the flow rate up to the WHP declines. Installing a Wellhead Compressor (WC) on the WHP is the solution [9]. The WC acts locally on the selected wells and reduces back pressure, thereby substantially enhancing the efficiency of oil and gas recovery [21]. The WC sensors transmit data back to the historian time series database, and intelligent alarm systems are utilized as a critical tool to minimize unscheduled downtime which adversely affects production reliability, as well as monitoring time and cost burden of operating engineers. In this paper, an Attention-Based Bidirectional Long Short-Term Memory (ABD-LSTM) model is presented for WC failure prediction. We also propose feature extraction and data reduction techniques as complementary methods to improve the effectiveness of the training process in a large-scale dataset. We evaluate our model performance based on real WC sensor data. Compared to other Machine Learning (ML) algorithms, our proposed methodology is more powerful and accurate. Our proposed ABD-LSTM achieved an optimal F1 score of 85.28%.","PeriodicalId":409298,"journal":{"name":"Proceedings of the 2020 the 4th International Conference on Compute and Data Analysis","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122540344","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}
Cyberbullying is becoming a huge problem on social media platforms. New statistics shows that more than a fourth of Norwegiankids report that they have been cyberbullied once or more duringthe last year. In the most recent years, it has become popularto utilize Neural Networks in order to automate the detection ofcyberbullying. These Neural Networks are often based on using Long-Short-Term-Memory layers solely or in combination withother types of layers. In this thesis we present a new Neural Networkdesign that can be used to detect traces of cyberbullying intextual media. The design is based on existing designs that combinesthe power of Convolutional layers with Long-Short-Term-Memorylayers. In addition, our design features the usage of stacked corelayers, which our research shows to increases the performance ofthe Neural Network. The design also features a new kind of activationmechanism, which is referred to as "Support-Vector-Machinelike activation". The "SupportVector-Machine like activation" isachieved by applying L2 weight regularization and utilizing a linearactivation function in the activation layer together with using aHinge loss function. Our experiments show that both the stackingof the layers and the "Support-Vector-Machine like activation"increasesthe performance of the Neural Network over traditionalState-Of-The-Art designs.
{"title":"Automated Cyberbullying Detection in Social Media Using an SVM Activated Stacked Convolution LSTM Network","authors":"Thor Aleksander Buan, Raghavendra Ramachandra","doi":"10.1145/3388142.3388147","DOIUrl":"https://doi.org/10.1145/3388142.3388147","url":null,"abstract":"Cyberbullying is becoming a huge problem on social media platforms. New statistics shows that more than a fourth of Norwegiankids report that they have been cyberbullied once or more duringthe last year. In the most recent years, it has become popularto utilize Neural Networks in order to automate the detection ofcyberbullying. These Neural Networks are often based on using Long-Short-Term-Memory layers solely or in combination withother types of layers. In this thesis we present a new Neural Networkdesign that can be used to detect traces of cyberbullying intextual media. The design is based on existing designs that combinesthe power of Convolutional layers with Long-Short-Term-Memorylayers. In addition, our design features the usage of stacked corelayers, which our research shows to increases the performance ofthe Neural Network. The design also features a new kind of activationmechanism, which is referred to as \"Support-Vector-Machinelike activation\". The \"SupportVector-Machine like activation\" isachieved by applying L2 weight regularization and utilizing a linearactivation function in the activation layer together with using aHinge loss function. Our experiments show that both the stackingof the layers and the \"Support-Vector-Machine like activation\"increasesthe performance of the Neural Network over traditionalState-Of-The-Art designs.","PeriodicalId":409298,"journal":{"name":"Proceedings of the 2020 the 4th International Conference on Compute and Data Analysis","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134533783","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}
The solvency of captive insurance is the key financial metric captive managers care about. We built a solvency prediction model for a captive insurance fund using Monte Carlo simulation with the fund's historical losses, current financial data and setups. This model can predict the solvency score of the current captive fund using the fund survival probability as a measurement of solvency. If the simulated future solvency ratios break the upper and lower bounds, we count it as an insolvent case; otherwise, it is counted a solvent (or survival) case. After large scale simulation, we can approximate the future survival probability, i.e. the solvency score, of the current captive fund. The predicted income statements, the balance sheets and financial ratios, will also be generated. We use a heat-map to visualize the solvency score at each retention level so that it can provide support to captive insurance managers to make their decisions. This model is implemented in Excel VBA macro and MATLAB.
{"title":"Using Monte Carlo Simulation to Predict Captive Insurance Solvency","authors":"Lu Xiong, Don Hong","doi":"10.1145/3388142.3388171","DOIUrl":"https://doi.org/10.1145/3388142.3388171","url":null,"abstract":"The solvency of captive insurance is the key financial metric captive managers care about. We built a solvency prediction model for a captive insurance fund using Monte Carlo simulation with the fund's historical losses, current financial data and setups. This model can predict the solvency score of the current captive fund using the fund survival probability as a measurement of solvency. If the simulated future solvency ratios break the upper and lower bounds, we count it as an insolvent case; otherwise, it is counted a solvent (or survival) case. After large scale simulation, we can approximate the future survival probability, i.e. the solvency score, of the current captive fund. The predicted income statements, the balance sheets and financial ratios, will also be generated. We use a heat-map to visualize the solvency score at each retention level so that it can provide support to captive insurance managers to make their decisions. This model is implemented in Excel VBA macro and MATLAB.","PeriodicalId":409298,"journal":{"name":"Proceedings of the 2020 the 4th International Conference on Compute and Data Analysis","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131353181","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}
For the conservation of energy in buildings, it is essential to understand the energy consumption pattern and make efforts based on the analyzed result for energy load reduction. In this research, we proposed a method for forecasting the electricity load of university buildings using a hybrid model of clustering technique and neural network using weather conditions. The novel approach discussed in this paper includes clustering one whole year data including the forecasting day using K-means clustering and using the result as an input parameter in a neural network for forecasting the electricity peak load of university buildings. The hybrid model has proved to increase the performance of forecasting rather than neural network alone. We also developed a graphical visualization platform for the analyzed result using an interactive web application called Shiny. Using Shiny application and forecasting electricity peak load with appreciable accuracy several hours before peak hours can aware the management authorities about the energy situation and provides sufficient time for making a strategy for peak load reduction. This method can also be implemented in the demand response for reducing the electricity bills by avoiding electricity usage during the high electricity rate hours.
{"title":"A Hybrid Model of Clustering and Neural Network Using Weather Conditions for Energy Management in Buildings","authors":"Bishnu Nepal, M. Yamaha","doi":"10.1145/3388142.3388172","DOIUrl":"https://doi.org/10.1145/3388142.3388172","url":null,"abstract":"For the conservation of energy in buildings, it is essential to understand the energy consumption pattern and make efforts based on the analyzed result for energy load reduction. In this research, we proposed a method for forecasting the electricity load of university buildings using a hybrid model of clustering technique and neural network using weather conditions. The novel approach discussed in this paper includes clustering one whole year data including the forecasting day using K-means clustering and using the result as an input parameter in a neural network for forecasting the electricity peak load of university buildings. The hybrid model has proved to increase the performance of forecasting rather than neural network alone. We also developed a graphical visualization platform for the analyzed result using an interactive web application called Shiny. Using Shiny application and forecasting electricity peak load with appreciable accuracy several hours before peak hours can aware the management authorities about the energy situation and provides sufficient time for making a strategy for peak load reduction. This method can also be implemented in the demand response for reducing the electricity bills by avoiding electricity usage during the high electricity rate hours.","PeriodicalId":409298,"journal":{"name":"Proceedings of the 2020 the 4th International Conference on Compute and Data Analysis","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115368722","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}
With the development of science and technology, L3 intelligent vehicles are gradually entering the mass production phase. Traditional testing tools and methods can hardly meet the requirements for multiple dimensions, high standard and big data of self-driving vehicles. The scenario-based simulation test method has great technical advantages in terms of test efficiency, verification cost and versatility, and is an important means for automatic driving test verification. However, it has shortcomings such as long scenario construction period and large repeatability. This paper is compiled based on secondary development of the automatic driving simulation software Panosim and presenting the automatic inputting of scenario and rapid adjustment of parameters through the digital twinning technology. In addition, the natural driving scenario database of China Automotive Technology and Research Center is used for verification. The results show that this method can improve the efficiency and accuracy of scenario construction, and greatly shorten the cycle of simulation test.
{"title":"Research on Automatic Generation Method of Scenario Based on Panosim","authors":"Zhang Lu, Zhibin Du, Xianglei Zhu","doi":"10.1145/3388142.3388165","DOIUrl":"https://doi.org/10.1145/3388142.3388165","url":null,"abstract":"With the development of science and technology, L3 intelligent vehicles are gradually entering the mass production phase. Traditional testing tools and methods can hardly meet the requirements for multiple dimensions, high standard and big data of self-driving vehicles. The scenario-based simulation test method has great technical advantages in terms of test efficiency, verification cost and versatility, and is an important means for automatic driving test verification. However, it has shortcomings such as long scenario construction period and large repeatability. This paper is compiled based on secondary development of the automatic driving simulation software Panosim and presenting the automatic inputting of scenario and rapid adjustment of parameters through the digital twinning technology. In addition, the natural driving scenario database of China Automotive Technology and Research Center is used for verification. The results show that this method can improve the efficiency and accuracy of scenario construction, and greatly shorten the cycle of simulation test.","PeriodicalId":409298,"journal":{"name":"Proceedings of the 2020 the 4th International Conference on Compute and Data Analysis","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122609241","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}
Social media, is often the go-to place where people discuss their opinions and share their feelings. As some platforms provide more anonymity than others, users have taken advantage of that privilege, by sitting behind the screen, the use of profanity has been able to create a toxic environment. Although not all profanities are used to offend people, it is undeniable that the anonymity has allowed social media users to express themselves more freely, increasing the likelihood of swearing. In this study, the use of profanity by different gender classes is compiled, and the findings showed that different genders often employ swear words from different hate categories, e.g. males tend to use more terms from the "disability" hate group. Classification models have been developed to predict the gender of tweet authors, and results showed that profanity could be used to uncover the gender of anonymous users. This shows the possibility that profiling of cyberbullies can be done from the aspect of gender based on profanity usage.
{"title":"How Different Genders Use Profanity on Twitter?","authors":"S. Wong, P. Teh, Chi-Bin Cheng","doi":"10.1145/3388142.3388145","DOIUrl":"https://doi.org/10.1145/3388142.3388145","url":null,"abstract":"Social media, is often the go-to place where people discuss their opinions and share their feelings. As some platforms provide more anonymity than others, users have taken advantage of that privilege, by sitting behind the screen, the use of profanity has been able to create a toxic environment. Although not all profanities are used to offend people, it is undeniable that the anonymity has allowed social media users to express themselves more freely, increasing the likelihood of swearing. In this study, the use of profanity by different gender classes is compiled, and the findings showed that different genders often employ swear words from different hate categories, e.g. males tend to use more terms from the \"disability\" hate group. Classification models have been developed to predict the gender of tweet authors, and results showed that profanity could be used to uncover the gender of anonymous users. This shows the possibility that profiling of cyberbullies can be done from the aspect of gender based on profanity usage.","PeriodicalId":409298,"journal":{"name":"Proceedings of the 2020 the 4th International Conference on Compute and Data Analysis","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116444465","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}
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