Air-combat behavior data mining based on truncation method

This paper considers the problem of applying data mining techniques to aeronautical field.The truncation method,which is one of the techniques in the aeronautical data mining,can be used to efficiently handle the air-combat behavior data.The technique of air-combat behavior data mining based on the truncation method is proposed to discover the air-combat rules or patterns.The simulation platform of the air-combat behavior data mining that supports two fighters is implemented.The simulation experimental results show that the proposed air-combat behavior data mining technique based on the truncation method is feasible whether in efficiency or in effectiveness.

Efficient Intelligent E-Learning Behavior-Based Analytics of Student’s Performance Using Deep Forest Model

E-learning behavior data indicates several students’activities on the e-learning platform such as the number of accesses to a set of resources and number of participants in lectures.This article proposes a new analytics systemto support academic evaluation for students via e-learning activities to overcome the challenges faced by traditional learning environments.The proposed e-learning analytics system includes a new deep forest model.It consists of multistage cascade random forests with minimal hyperparameters compared to traditional deep neural networks.The developed forest model can analyze each student’s activities during the use of an e-learning platform to give accurate expectations of the student’s performance before ending the semester and/or the final exam.Experiments have been conducted on the Open University Learning Analytics Dataset(OULAD)of 32,593 students.Our proposed deep model showed a competitive accuracy score of 98.0%compared to artificial intelligence-based models,such as ConvolutionalNeuralNetwork(CNN)and Long Short-TermMemory(LSTM)in previous studies.That allows academic advisors to support expected failed students significantly and improve their academic level at the right time.Consequently,the proposed analytics system can enhance the quality of educational services for students in an innovative e-learning framework.

Analysis of Stopping Behavior at Rural T-Intersections Using Naturalistic Driving Study Data

Rural intersections account for around 30% of crashes in rural areas and 6% of all fatal crashes, representing a significant but poorly understood safety problem. Crashes at rural intersections are also problematic since high speeds on intersection approaches are present which can exacerbate the impact of a crash. Additionally, rural areas are often underserved with EMS services which can further contribute to negative crash outcomes. This paper describes an analysis of driver stopping behavior at rural T-intersections using the SHRP 2 Naturalistic Driving Study data. Type of stop was used as a safety surrogate measure using full/rolling stops compared to non-stops. Time series traces were obtained for 157 drivers at 87 unique intersections resulting in 1277 samples at the stop controlled approach for T-intersections. Roadway (i.e. number of lanes, presence of skew, speed limit, presence of stop bar or other traffic control devices), driver (age, gender, speeding), and environmental characteristics (time of day, presence of rain) were reduced and included as independent variables. Results of a logistic regression model indicated drivers were less likely to stop during the nighttime. However presence of intersection lighting increased the likelihood of full/rolling stops. Presence of intersection skew was shown to negatively impact stopping behavior. Additionally drivers who were traveling over the posted speed limit upstream of the intersection approach were less likely to stop at the approach stop sign.

The Intersection of Privacy by Design and Behavioral Economics: Nudging Users towards Privacy-Friendly Choices

This paper conducts a comprehensive review of existing research on Privacy by Design (PbD) and behavioral economics, explores the intersection of Privacy by Design (PbD) and behavioral economics, and how designers can leverage “nudges” to encourage users towards privacy-friendly choices. We analyze the limitations of rational choice in the context of privacy decision-making and identify key opportunities for integrating behavioral economics into PbD. We propose a user-centered design framework for integrating behavioral economics into PbD, which includes strategies for simplifying complex choices, making privacy visible, providing feedback and control, and testing and iterating. Our analysis highlights the need for a more nuanced understanding of user behavior and decision-making in the context of privacy, and demonstrates the potential of behavioral economics to inform the design of more effective PbD solutions.

Ending Privacy’s Gremlin: Stopping the Data-Broker Loophole to the Fourth Amendment’s Search Warrant Requirement

Advances in technology require upgrades in the law. One such area involves data brokers, which have thus far gone unregulated. Data brokers use artificial intelligence to aggregate information into data profiles about individual Americans derived from consumer use of the internet and connected devices. Data profiles are then sold for profit. Government investigators use a legal loophole to purchase this data instead of obtaining a search warrant, which the Fourth Amendment would otherwise require. Consumers have lacked a reasonable means to fight or correct the information data brokers collect. Americans may not even be aware of the risks of data aggregation, which upends the test of reasonable expectations used in a search warrant analysis. Data aggregation should be controlled and regulated, which is the direction some privacy laws take. Legislatures must step forward to safeguard against shadowy data-profiling practices, whether abroad or at home. In the meantime, courts can modify their search warrant analysis by including data privacy principles.

Exploring users' within-site navigation behavior:A case study based on clickstream data

Purpose:The goal of our research is to suggest specific Web metrics that are useful for evaluating and improving user navigation experience on informational websites.Design/methodology/approach:We revised metrics in a Web forensic framework proposed in the literature and defined the metrics of footprint,track and movement.Data were obtained from user clickstreams provided by a real estate site’s administrators.There were two phases of data analysis with the first phase on navigation behavior based on user footprints and tracks,and the second phase on navigational transition patterns based on user movements.Findings:Preliminary results suggest that the apartment pages were heavily-trafficked while the agent pages and related information pages were underused to a great extent.Navigation within the same category of pages was prevalent,especially when users navigated among the regional apartment listings.However,navigation of these pages was found to be inefficient.Research limitations:The suggestions for navigation design optimization provided in the paper are specific to this website,and their applicability to other online environments needs to be verified.Preference predications or personal recommendations are not made during the current stage of research.Practical implications:Our clickstream data analysis results offer a base for future research.Meanwhile,website administrators and managers can make better use of the readily available clickstream data to evaluate the effectiveness and efficiency of their site navigation design.Originality/value:Our empirical study is valuable to those seeking analysis metrics for evaluating and improving user navigation experience on informational websites based on clickstream data.Our attempts to analyze the log file in terms of footprint,track and movement will enrich the utilization of such trace data to engender a deeper understanding of users’within-site navigation behavior.

Using microscopic video data measures for driver behavior analysis during adverse winter weather:opportunities and challenges

This paper presents a driver behavior analysis using microscopic video data measures including vehicle speed, lane-changing ratio, and time to collision. An analytical framework was developed to evaluate the effect of adverse winter weather conditions on highway driving behavior based on automated （computer） and manual methods. The research was conducted through two case studies. The first case study was conducted to evaluate the feasibility of applying an au- tomated approach to extracting driver behavior data based on 15 video recordings obtained in the winter 2013 at three dif- ferent locations on the Don Valley Parkway in Toronto, Canada. A comparison was made between the automated approach and manual approach, and issues in collecting data using the automated approach under winter conditions were identified. The second case study was based on high quality data collected in the winter 2014, at a location on Highway 25 in Montreal, Canada. The results demonstrate the effectiveness of the automated analytical framework in analyzing driver behavior, as well as evaluating the impact of adverse winter weather conditions on driver behavior. This approach could be applied to evaluate winter maintenance strategies and crash risk on highways during adverse winter weather conditions.

基于多模态数据的智慧课堂学生学习行为研究

新兴技术赋能下的智慧课堂为学生学习行为监测、评价、反馈、预警提供丰富的教育多模态数据,充分采集、处理和分析教育多模态数据能够为教育研究和实践提供参考。首先,梳理已有智慧课堂学习行为分析和多模态数据教学研究,借鉴学习行为分类系统,构建基于多模态数据类型的“声音数据—图像数据—文本数据”三维的多模态数据分析框架,从言语学习活动、位置移动、身体动作、技术使用四个方面呈现智慧课堂下学生学习行为变化;其次,将体现学生学习行为特征的多模态数据编码,并进行质性表征形成编码体系;最后,从情境和时序行为分析视角出发,对课例中的学习行为多模态数据进行频度变化及周期性变化分析。研究得出,在智慧课堂环境下,学生课堂学习参与性、主动性、专注度均在不断提升,课堂学习氛围朝着积极的方向发展,无效及无关学习行为逐渐减少。此外,通过对学生学习行为多模态数据的剖析,以期为学生了解自身学习行为及状态、教师进行个性化教学,以及研究者构建学习评价体系提供数据来源、科学依据。

程序调试中的数据结构演变可视化方法

数据结构是程序执行时的重要信息载体,其演变过程往往较为复杂和抽象。针对程序设计初学者较难理解数据结构演变过程的问题,提出一种面向程序调试中的数据结构演变可视化方法。在调试开始时,首先使用组件表达式表示数据结构,然后通过配置表以及布局函数将组件表达式生成可视化视图。在进行单步调试后,通过对比单步调试前后的可视化数据方式生成组件演变行为,运用有限状态机理论构建数据结构演变模型,该模型的执行使得组件发生演变,同时生成可视化动作。使用动画单元执行可视化动作,实现数据结构演变的可视化呈现,帮助初学者理解数据结构相关程序的执行过程。实验结果表明,将该方法应用于1个数据结构演变可视化框架StructV,并应用于1个在线可视化编程实训云平台的研发,可有效降低初学者理解数据结构演变过程的难度,并提升编程实训时的程序调试效率。

大数据环境下多模态融合的大学生异常行为预警

针对“校园大数据”累积的海量数据呈现出离散性、稀疏性等问题,如何从基数大、活动广、个性强的校园学生群体中检测出潜在的、有异常行为的学生,已成为学生异常行为分析亟需解决的问题.本文提出了一种大数据环境下基于多模态融合的大学生异常行为预警方法(early warning method for abnormal behavior of college students based on multi-modal fusion in big data environment,EWMAB).首先,针对学生行为画像的表征不够丰富,行为标签存在时效性、动态性等问题,建立一种基于多模态特征深度学习的跨模态学生行为画像模型;其次,针对学生异常行为预测、预警的时效性和后置性问题,在学生行为画像和学生行为分类预测基础上,提出了一种基于多模态融合的学生异常行为预警方法,通过长短期记忆神经网络(long and short term memory networks,LSTM),结合学生行为多指标数据和文本信息来解决学生异常行为预警问题;最后,本文通过应用实例验证模型以学生学习成绩异常预警为例,与其他预警算法相比,EWMAB方法可以提高预警的准确性,实现学生异常行为预警的时效性和前置性,从而使学生教育工作更具有针对性、个性化和预测性.

数据驱动的新能源公交车能耗预测

鉴于现有电动车能耗预测多基于实验室条件,存在结果过于理想化或预测准确度不足的问题。本文基于北京市51路公交车的实车运行数据,分析能耗影响因素,通过时钟循环编码优化时间信息、使用箱线图设置阈值以构造行驶工况、建立基于熵权法的驾驶行为评价体系对驾驶行为与工况状态进行辅助分析,最后,对聚类后的4类典型工况片段分别建立引入注意力机制的LSTM能耗预测模型,并将其与传统LSTM及LGBM等多种预测模型进行对比分析,验证结果表明引入注意力机制的LSTM预测模型性能显著高于其他模型。

企业数据的知识产权保护

企业数据和知识产权的客体之间存有共性。二者的保护基础理论相互融通,并且企业数据权利与知识产权权利结构相近似,这为企业数据的知识产权保护奠定了基础。检视现有知识产权制度框架下保护模式的有限性以识别现有制度与企业数据保护问题之间真正无法重合的地带。构建和完善企业数据保护规则须对企业数据进行类型化结构,基于类型化区分和场景化差异探寻不同保护路径。从解释论角度而言,对于无法纳入既有法定权利范畴的企业数据权益,适用《反不正当竞争法》一般条款规制未被类型化权益,关键在于客观审查数据使用行为是否违反商业道德。从立法论角度而言,可参照知识产权的行为规制赋权化路径,将规制焦点建立在对企业数据的利用行为上,对利用行为进行类型化提炼,建构一种以禁止权和报酬权为核心的不完满的控制权和有限度的排他权。

“互联网+”时代背景下智慧课堂交互行为数据精准搜索方法

在智慧课堂环境中,交互行为数据的复杂性难以保障准确的搜索结果。因此,在“互联网+”时代背景下,文章提出对智慧课堂交互行为数据进行精准搜索的方法并构建了一个智慧课堂交互行为数据分类模型,从“人-人交互”和“人-内容交互”2个维度深入分析智慧课堂交互行为数据的构成。为实现精准搜索,文章设置了带有唯一标签的智慧课堂交互行为数据染色体,基于目标对象的唯一标签,精准地确定搜索结果。测试结果显示,该方法能够显著提升交互行为数据的搜索精准性,与对照组相比具有明显优势,查全率偏差基本稳定在5.0%以内,最大偏差仅为6.0%,最小偏差达到了0。

政府数据不当使用行为的影响路径研究

[目的/意义]任何事物的发展都有两面性,探讨政府数据不当使用行为的影响因素及其作用机制,有助于充分发挥政府数据价值,为加快“数字中国”的建设提供参考依据。[方法/过程]运用元分析对政府数据不当使用行为相关实证研究文献进行分析,提取影响因素,分析影响效应,确定条件变量;在确定条件变量的基础上,基于典型案例运用fsQCA探讨各影响因素的组态路径。[结果/结论]政府数据不当使用行为的9个影响因素之间形成8条影响路径,并呈现三种组合效用类型,分别为“多要素复合驱动型”“技术主导型”“内外双重加持型”。其影响效应是:政府数据不当使用行为的发生与核心条件不存在必然联系;技术发展水平在政府数据不当使用行为的发生机制里发挥重要作用;外部社会影响的复杂性和内部数据质量的多面性使得多因素之间形成不同的作用机制。

数据资源持有权的法律解释

在大数据和人工智能技术的推动下,数据资源从单纯的信息载体转变为独立的价值来源,开始出现财产属性。但其所具有的非排他性、技术依附性等特征使得传统的信息权益与权利保护范式难以适用。数据资源的价值源于对数据的控制状态,这并非实体权利。数据资源持有权在法律上应解释为“持有主体合法控制数据资源并免受非法干扰和攻击的利益”。持有利益受侵害所造成的损失属于纯粹经济损失,应采用行为主义保护模式,通过侵权法一般条款进行救济。持有主体则应根据具体数据类型遵守相应的行为标准。此外,个人信息数据的相关规则是基于风险防范的考量,对数据所携带信息采取保护措施,个人信息主体属于数据来源者而非数据资源持有主体。

有限维空间下运动行为传感数据特征提取

维度的升高会加剧运动行为传感数据的复杂度,导致其分布特征空间被无限放大,因此提出基于有限维空间的运动行为传感数据特征提取方法。采用关联规则项挖掘分析方法计算数据模糊度,确定运动行为的有限空间区域。在有限维空间下,通过自适应寻优方法,计算传感数据的特征量化参数。检测运动行为传感数据的特征属性,计算数据分布融合映射输出结果,构建运动行为特征提取模型。实验结果表明,所提方法的运动数据空间聚类效果较好,能够把数据固定在有限维空间,数据特征提取精度始终保持在95%以上。

在线教育学习者学习有效性评价及差异性研究

在线教育具有开放性和共享性特征,深入挖掘在线学习者学习行为,能够有助于学生及时了解学习效果。选取在线教育平台思政类、外语类和管理类课程为对象,研究在线学习者的学习有效性及群体差异性特征。研究结果表明,不同类型课程的学习者行为和成绩分布具有相似性,大部分学习者并未实现高效的学习效率,学习行为多且成绩较好的“勤奋收获者”与“学习体验者”学习效率之间存在明显的差异性。基于此,提出在线教育的过程中,应当加强阶段性监督,对缺乏学习积极性的学生进行及时干预,完善线上教育的筛选制度等措施,以期提高学习者的效率水平和发挥在线课程的最大利用程度。

基于学习通平台的学习行为分析研究

文章基于学习通平台应用于《水资源规划及利用》混合式教学改革实践中获取的大数据对教育对象的学习行为进行分析,并对课程考核中涉及学习通平台的各项成绩与总评成绩的相关性进一步梳理,探讨现代教育技术应用于混合式教学的优势及存在的困难。研究结果表明:混合式教学过程中的线上自学对自制力差的学生带来了较大的挑战,线上成绩与期末成绩的数据之间缺少显著相关性,这可能与学生的自制力、诚信、自学能力、心理等多方面因素有关。

智慧图书馆服务体系构建

随着互联网技术的快速发展和应用,公共图书馆从传统的基于馆藏资源以借阅为主的服务模式,转变为现今的基于用户体验的数字图书馆乃至智慧图书馆。为做好智慧图书馆建设,实现以读者需求为导向的服务模式,我们需要了解智慧图书馆的本质和特点,以及当前的发展现状和存在的问题,全方位构建智慧图书馆服务体系,并对其将来的发展树立明确的思路。

关于行为安全观察与人员安全的探索研究

开展行为安全观察与沟通,掌握行为安全观察实施目的和意义,不断优化系统模型,深挖深层次的管理原因,找出管理背后的薄弱项并加以完善.通过各层级管理人员、行为性质分布、观察内容数量等11个维度的分析结果,可以有效反馈出问题指向,从而反向引导员工行为,降低不安全行为及习惯性违章造成的事故(未遂事故).