In-cab alerts warn commercial vehicle drivers of upcoming roadway incidents, slowdowns and work zone construction activities. This paper reports on a study evaluating the driver response to in-cab alerts in Ohio. Driv...In-cab alerts warn commercial vehicle drivers of upcoming roadway incidents, slowdowns and work zone construction activities. This paper reports on a study evaluating the driver response to in-cab alerts in Ohio. Driver response was evaluated by measuring the statistical trends of vehicle speeds after the in-cab alerts were received. Vehicle speeds pre and post in-cab alert were collected over a 47 day period in the fall of 2023 for trucks traveling on interstate roadways in Ohio. Results show that approximately 22% of drivers receiving Dangerous Slowdown alerts had reduced their speeds by at least 5 mph 30 seconds after receiving such an alert. Segmenting this analysis by speed found that of vehicles traveling at or above 70 mph at the time of alerting, 26% reduced speeds by at least 5 mph. These speed reductions suggest drivers taking actional measures after receiving alerts. Future studies will involve further analysis on the impact of the types of alerts shown, roadway characteristics and overall traffic conditions on truck speeds passing through work zones.展开更多
Security Information and Event Management (SIEM) platforms are critical for organizations to monitor and manage their security operations centers. However, organizations using SIEM platforms have several challenges su...Security Information and Event Management (SIEM) platforms are critical for organizations to monitor and manage their security operations centers. However, organizations using SIEM platforms have several challenges such as inefficiency of alert management and integration with real-time communication tools. These challenges cause delays and cost penalties for organizations in their efforts to resolve the alerts and potential security breaches. This paper introduces a cybersecurity Alert Distribution and Response Network (Adrian) system. Adrian introduces a novel enhancement to SIEM platforms by integrating SIEM functionalities with real-time collaboration platforms. Adrian leverages the uniquity of mobile applications of collaboration platforms to provide real-time alerts, enabling a two-way communication channel that facilitates immediate response to security incidents and efficient SIEM platform management. To demonstrate Adrian’s capabilities, we have introduced a case-study that integrates Wazuh, a SIEM platform, to Slack, a collaboration platform. The case study demonstrates all the functionalities of Adrian including the real-time alert distribution, alert customization, alert categorization, and enablement of management activities, thereby increasing the responsiveness and efficiency of Adrian’s capabilities. The study concludes with a discussion on the potential expansion of Adrian’s capabilities including the incorporation of artificial intelligence (AI) for enhanced alert prioritization and response automation.展开更多
Modern cloud services are monitored by numerous multidomain and multivendor monitoring tools,which generate massive numbers of alerts and events that are not actionable.These alerts usually carry isolated messages tha...Modern cloud services are monitored by numerous multidomain and multivendor monitoring tools,which generate massive numbers of alerts and events that are not actionable.These alerts usually carry isolated messages that are missing service contexts.Administrators become inundated with tickets caused by such alert events when they are routed directly to incident management systems.Noisy alerts increase the risk of crucial warnings going undetected and leading to service outages.One of the feasible ways to cope with the above problems involves revealing the correlations behind a large number of alerts and then aggregating the related alerts according to their correlations.Based on these guidelines,AlertInsight,a framework for alert event reduction,is proposed in this paper.In AlertInsight,the correlations among event sources are found by mining a sequence of historical events.Then,event correlation knowledge is employed to build an online detector targeting the correlated events that are hidden in the event stream.Finally,the correlated events are aggregated into a single high-level event for alert reduction.Because of theweaknesses of the commonly used pairwise correlation analysis methods in complex environments,an innovative approach for multiple correlation mining,which overcomes computational complexity challenges by scanning panoramic views of historical episodes from the perspective of holism,is proposed in this paper.In addition,a neural network-based correlated event detector that can learn the event correlation knowledge generated from correlation mining and then detect the correlated events in a sequence online is proposed.Experiments are conducted to test the effectiveness of AlertInsight.The experimental results(precision=0.92,recall=0.93,and F1-score=0.93)demonstrate the performance of AlertInsight for the recognition of multiple correlated alerts and its competence for alert reduction.展开更多
A large part of our daily lives is spent with audio information. Massive obstacles are frequently presented by the colossal amounts of acoustic information and the incredibly quick processing times. This results in th...A large part of our daily lives is spent with audio information. Massive obstacles are frequently presented by the colossal amounts of acoustic information and the incredibly quick processing times. This results in the need for applications and methodologies that are capable of automatically analyzing these contents. These technologies can be applied in automatic contentanalysis and emergency response systems. Breaks in manual communication usually occur in emergencies leading to accidents and equipment damage. The audio signal does a good job by sending a signal underground, which warrants action from an emergency management team at the surface. This paper, therefore, seeks to design and simulate an audio signal alerting and automatic control system using Unity Pro XL to substitute manual communication of emergencies and manual control of equipment. Sound data were trained using the neural network technique of machine learning. The metrics used are Fast Fourier transform magnitude, zero crossing rate, root mean square, and percentage error. Sounds were detected with an error of approximately 17%;thus, the system can detect sounds with an accuracy of 83%. With more data training, the system can detect sounds with minimal or no error. The paper, therefore, has critical policy implications about communication, safety, and health for underground mine.展开更多
为探究不同模态转换下任务切换对监控人员警觉度衰减现象的影响,采用脑电实验法,设计了危险动作识别、危险场景识别等任务,并结合行为数据及事件相关电位(Event Related Potential,ERP)技术进行综合分析;实验重点考察在任务切换过程中,...为探究不同模态转换下任务切换对监控人员警觉度衰减现象的影响,采用脑电实验法,设计了危险动作识别、危险场景识别等任务,并结合行为数据及事件相关电位(Event Related Potential,ERP)技术进行综合分析;实验重点考察在任务切换过程中,不同模态的任务之间进行切换对监控人员警觉度的具体影响;通过对煤矿监控调度人员的实证研究,揭示了切换任务的模态差异性对监控员警觉度的影响及其内在机制。结果表明:当在相同刺激任务间进行切换时带来的工作绩效下降,主要原因在于任务切换产生的切换成本导致注意力分散,具体体现在任务切换后P300和P200平均波幅的升高;但是在不同刺激任务间进行切换时,尤其是切换到听觉刺激任务时,被试者的警觉度水平反而得到了一定程度的提升。展开更多
文摘In-cab alerts warn commercial vehicle drivers of upcoming roadway incidents, slowdowns and work zone construction activities. This paper reports on a study evaluating the driver response to in-cab alerts in Ohio. Driver response was evaluated by measuring the statistical trends of vehicle speeds after the in-cab alerts were received. Vehicle speeds pre and post in-cab alert were collected over a 47 day period in the fall of 2023 for trucks traveling on interstate roadways in Ohio. Results show that approximately 22% of drivers receiving Dangerous Slowdown alerts had reduced their speeds by at least 5 mph 30 seconds after receiving such an alert. Segmenting this analysis by speed found that of vehicles traveling at or above 70 mph at the time of alerting, 26% reduced speeds by at least 5 mph. These speed reductions suggest drivers taking actional measures after receiving alerts. Future studies will involve further analysis on the impact of the types of alerts shown, roadway characteristics and overall traffic conditions on truck speeds passing through work zones.
文摘Security Information and Event Management (SIEM) platforms are critical for organizations to monitor and manage their security operations centers. However, organizations using SIEM platforms have several challenges such as inefficiency of alert management and integration with real-time communication tools. These challenges cause delays and cost penalties for organizations in their efforts to resolve the alerts and potential security breaches. This paper introduces a cybersecurity Alert Distribution and Response Network (Adrian) system. Adrian introduces a novel enhancement to SIEM platforms by integrating SIEM functionalities with real-time collaboration platforms. Adrian leverages the uniquity of mobile applications of collaboration platforms to provide real-time alerts, enabling a two-way communication channel that facilitates immediate response to security incidents and efficient SIEM platform management. To demonstrate Adrian’s capabilities, we have introduced a case-study that integrates Wazuh, a SIEM platform, to Slack, a collaboration platform. The case study demonstrates all the functionalities of Adrian including the real-time alert distribution, alert customization, alert categorization, and enablement of management activities, thereby increasing the responsiveness and efficiency of Adrian’s capabilities. The study concludes with a discussion on the potential expansion of Adrian’s capabilities including the incorporation of artificial intelligence (AI) for enhanced alert prioritization and response automation.
文摘Modern cloud services are monitored by numerous multidomain and multivendor monitoring tools,which generate massive numbers of alerts and events that are not actionable.These alerts usually carry isolated messages that are missing service contexts.Administrators become inundated with tickets caused by such alert events when they are routed directly to incident management systems.Noisy alerts increase the risk of crucial warnings going undetected and leading to service outages.One of the feasible ways to cope with the above problems involves revealing the correlations behind a large number of alerts and then aggregating the related alerts according to their correlations.Based on these guidelines,AlertInsight,a framework for alert event reduction,is proposed in this paper.In AlertInsight,the correlations among event sources are found by mining a sequence of historical events.Then,event correlation knowledge is employed to build an online detector targeting the correlated events that are hidden in the event stream.Finally,the correlated events are aggregated into a single high-level event for alert reduction.Because of theweaknesses of the commonly used pairwise correlation analysis methods in complex environments,an innovative approach for multiple correlation mining,which overcomes computational complexity challenges by scanning panoramic views of historical episodes from the perspective of holism,is proposed in this paper.In addition,a neural network-based correlated event detector that can learn the event correlation knowledge generated from correlation mining and then detect the correlated events in a sequence online is proposed.Experiments are conducted to test the effectiveness of AlertInsight.The experimental results(precision=0.92,recall=0.93,and F1-score=0.93)demonstrate the performance of AlertInsight for the recognition of multiple correlated alerts and its competence for alert reduction.
文摘A large part of our daily lives is spent with audio information. Massive obstacles are frequently presented by the colossal amounts of acoustic information and the incredibly quick processing times. This results in the need for applications and methodologies that are capable of automatically analyzing these contents. These technologies can be applied in automatic contentanalysis and emergency response systems. Breaks in manual communication usually occur in emergencies leading to accidents and equipment damage. The audio signal does a good job by sending a signal underground, which warrants action from an emergency management team at the surface. This paper, therefore, seeks to design and simulate an audio signal alerting and automatic control system using Unity Pro XL to substitute manual communication of emergencies and manual control of equipment. Sound data were trained using the neural network technique of machine learning. The metrics used are Fast Fourier transform magnitude, zero crossing rate, root mean square, and percentage error. Sounds were detected with an error of approximately 17%;thus, the system can detect sounds with an accuracy of 83%. With more data training, the system can detect sounds with minimal or no error. The paper, therefore, has critical policy implications about communication, safety, and health for underground mine.
文摘为探究不同模态转换下任务切换对监控人员警觉度衰减现象的影响,采用脑电实验法,设计了危险动作识别、危险场景识别等任务,并结合行为数据及事件相关电位(Event Related Potential,ERP)技术进行综合分析;实验重点考察在任务切换过程中,不同模态的任务之间进行切换对监控人员警觉度的具体影响;通过对煤矿监控调度人员的实证研究,揭示了切换任务的模态差异性对监控员警觉度的影响及其内在机制。结果表明:当在相同刺激任务间进行切换时带来的工作绩效下降,主要原因在于任务切换产生的切换成本导致注意力分散,具体体现在任务切换后P300和P200平均波幅的升高;但是在不同刺激任务间进行切换时,尤其是切换到听觉刺激任务时,被试者的警觉度水平反而得到了一定程度的提升。
