Decision-Making in Driver-Automation Shared Control:A Review and Perspectives

Shared control schemes allow a human driver to work with an automated driving agent in driver-vehicle systems while retaining the driver’s abilities to control.The human driver,as an essential agent in the driver-vehicle shared control systems,should be precisely modeled regarding their cognitive processes,control strategies,and decision-making processes.The interactive strategy design between drivers and automated driving agents brings an excellent challenge for human-centric driver assistance systems due to the inherent characteristics of humans.Many open-ended questions arise,such as what proper role of human drivers should act in a shared control scheme?How to make an intelligent decision capable of balancing the benefits of agents in shared control systems?Due to the advent of these attentions and questions,it is desirable to present a survey on the decision making between human drivers and highly automated vehicles,to understand their architectures,human driver modeling,and interaction strategies under the driver-vehicle shared schemes.Finally,we give a further discussion on the key future challenges and opportunities.They are likely to shape new potential research directions.

Observation-Oriented Causal Discovery for Cultivation Abandonment of Rice Terraces: Focusing on an Effect of Cultural Endemism on Decision-Making in Toraja, Indonesia

Previous discussion about the factors of the expanding trend of abandoned cultivation had focused only on universal factors and lacked evaluation of the regionality of the phenomenon. This paper demonstrated the Toraja’s regional characteristics and the influence of cultural endemism on decision-making about abandoning cultivation by an observation-oriented approach. Based on a causal framework constructed by field observation and geospatial data generation, an adjustment for overt covariates using the multivariate logistic regression model to draw the causal effect from hidden covariates was examined in two rice terraces with different water systems, i.e. irrigated field and rain-fed field. The result of sub-group analysis revealed that decisions about abandoning cultivation in Toraja were greatly associated with disadvantageous factors for intensive farming, i.e. “number of adjacent fields” and “soil erosion” rather than advantageous factors, i.e. “area of field” and “distance to roads”. Moreover, the result of interaction analysis which controlled the effect of topography revealed the powerful effect of particular decision factors only in rain-fed rice terrace: the “distance to roads” factor’s fairly negative contribution on abandoning cultivation (Odds ratio = 9.94E - 01, P value = 2.03E - 11), as well as the “number of adjacent field” factor’s positive contribution on abandoning cultivation (Odds ratio = 1.13E+00, P value = 3.65E - 04). Given the evidence from the explanation of these results by customary laws and land inheritance system for each site, therefore, it could be concluded that the screening and detection of cultural endemism’s influence was achieved using the algorithm this paper proposes.

上市公司互动式信息沟通能够降低股票错误定价吗?

资本市场本质上是一个信息市场,信息是决定上市公司股票价值最重要的因素之一。随着信息技术的运用和市场成熟度的提高,上市公司与投资者之间的信息传递模式正在由“单向式信息披露”向“互动式信息沟通”转变。本文考察了我国上市公司与投资者新型互动式信息沟通行为对股票市场定价效率的影响。研究发现:上市公司与投资者的互动式信息沟通行为越积极,即回复率越高、回复越及时,公司股票的错误定价程度越低;公司与投资者的积极互动沟通行为主要通过减少投资者异质信念、增加股价信息含量和改善公司信息环境来降低股票错误定价水平;进一步结合我国股票市场特征发现,这种互动沟通产生的定价效率优化效应在投资者情绪较强、机构投资者持股较少的公司中更加显著。本文的研究结论证实了我国交易所监管下互动沟通平台的市场有效性,同时也为进一步优化市场资源配置的效果提供了新的经验证据。

空调温控负荷集群参与光伏消纳的潜力评估与互动框架

空调温控负荷集群作为当下最具调节潜力的需求侧响应资源之一,在削峰、填谷、分布式光伏消纳和电网调控中将发挥重要作用。因此,提出一种电力市场环境下,基于数据驱动和深度置信网络的空调温控负荷集群参与分布式光伏消纳的可调节潜力评估与互动框架。首先,利用数据驱动构建了基于深度置信网络的可调节潜力评估模型,实时输出温控负荷集群的可调节潜力;其次,考虑功率调整量在一定范围内变化的前提下,构建基于深度置信网络的需求互动模型,对温控负荷集群进行实时温度调控。最后,以冀北地区某10kV馈线作为实际算例进行分析,结果表明:所提框架能够充分利用空调温控负荷集群的可调节潜力,参与分布式光伏的消纳。

以互动达标理论为指导的延续性护理结合知信行干预对妊娠期糖尿病患者血糖控制及健康行为的影响

目的研究以互动达标理论为指导的延续性护理结合知信行干预对妊娠期糖尿病(GDM)患者血糖控制及健康行为的影响。方法择取2020年3月至2022年3月收治的80例GDM患者为研究对象,随机将其分为对照组和观察组,每组40例。对照组采用常规护理干预,观察组在对照组基础上加施以互动达标理论为指导的延续性护理结合知信行干预。比较两组的干预效果。结果干预后,观察组的空腹血糖(FBG)、餐后2 h血糖(2 h PBG)水平及糖化血红蛋白(HbA1c)低于对照组(P<0.05);观察组的血糖控制达标率高于对照组(P<0.05)。干预后,观察组的健康促进生活方式量表-Ⅱ(HPLP-Ⅱ)各维度评分高于对照组(P<0.05)。干预后,观察组的自我护理能力测定量表(ESCA)各维度评分高于对照组(P<0.05)。结论以互动达标理论为指导的延续性护理结合知信行干预不仅能够有效改善GDM患者血糖控制及健康行为,还能提高其自护能力。

Significance of Live Streaming in Shaping Business: A Critical Review and Analytical Study

With the rise of live streaming on social media, platforms like Facebook, Instagram, and YouTube have become powerful business tools. They enable users to share live videos, fostering direct connections between businesses and their customers. This critical literature review paper explores the impact of live streaming on businesses, focusing on its role in attracting and satisfying consumers by promoting products tailored to their needs and wants. It emphasizes live streaming’s crucial role in engaging customers, a key to business growth. The study also provides viable strategies for businesses to leverage live streaming for growth and customer engagement, underscoring its importance in the business landscape.

基于健康信念的护理干预结合互动达标式康复护理对慢性阻塞性肺疾病患者康复方案执行及自护能力的影响

目的探讨基于健康信念的护理干预结合互动达标式康复护理在慢性阻塞性肺疾病(COPD)中的应用效果。方法选取2019年3月至2022年3月收治的120例COPD患者为研究对象,以随机数字表法将其分为对照组和观察组,各60例。对照组施以常规护理,观察组施以基于健康信念的护理干预结合互动达标式康复护理。比较两组的干预效果。结果干预后,观察组的合理饮食、康复训练、遵医嘱用药执行率均高于对照组(P<0.05)。干预后,观察组的自护能力测定量表(ESCA)各维度评分高于对照组(P<0.05)。干预后,观察组的呼气峰流速(PEF)、最大肺活量(VCmax)高于对照组,差异具有统计学意义(P<0.05)。结论基于健康信念的护理干预结合互动达标式康复护理可提高COPD患者康复方案执行率和自护能力,促进肺功能改善,值得推广。

《聊斋志异》中关帝信仰与民间信仰的互动关系研究

《聊斋志异》在书写关帝信仰时,对关帝信仰与其他七种民间信仰的互动关系进行了艺术化的描写。在该书中,关帝信仰作为国家正祀的一面得到凸显,并分别与三类民间信仰产生互动:一是包括周仓信仰和城隍信仰在内的异化的官方信仰,二是包括雹神信仰和蚩尤崇拜在内的地方神灵信仰,三是包括狐狸崇拜、青蛙神崇拜、猪婆龙崇拜在内的民间动物崇拜。关帝信仰与它们的互动呈现出既有合作与共存、又有管控与压制的复杂关系。

Game Interactive Learning:A New Paradigm towards Intelligent Decision-Making

Decision-making plays an essential role in various real-world systems like automatic driving,traffic dispatching,information system management,and emergency command and control.Recent breakthroughs in computer game scenarios using deep reinforcement learning for intelligent decision-making have paved decision-making intelligence as a burgeoning research direction.In complex practical systems,however,factors like coupled distracting features,long-term interact links,and adversarial environments and opponents,make decision-making in practical applications challenging in modeling,computing,and explaining.This work proposes game interactive learning,a novel paradigm as a new approach towards intelligent decision-making in complex and adversarial environments.This novel paradigm highlights the function and role of a human in the process of intelligent decision-making in complex systems.It formalizes a new learning paradigm for exchanging information and knowledge between humans and the machine system.The proposed paradigm first inherits methods in game theory to model the agents and their preferences in the complex decision-making process.It then optimizes the learning objectives from equilibrium analysis using reformed machine learning algorithms to compute and pursue promising decision results for practice.Human interactions are involved when the learning process needs guidance from additional knowledge and instructions,or the human wants to understand the learning machine better.We perform preliminary experimental verification of the proposed paradigm on two challenging decision-making tasks in tactical-level War-game scenarios.Experimental results demonstrate the effectiveness of the proposed learning paradigm.

A novel interactive preferential evolutionary method for controller tuning in chemical processes

In response to many multi-attribute decision-making(MADM)problems involved in chemical processes such as controller tuning,which suffer human's subjective preferential nature in human–computer interactions,a novel affective computing and preferential evolutionary solution is proposed to adapt human–computer interaction mechanism.Based on the stimulating response mechanism,an improved affective computing model is introduced to quantify decision maker's preference in selections of interactive evolutionary computing.In addition,the mathematical relationship between affective space and decision maker's preferences is constructed.Subsequently,a human–computer interactive preferential evolutionary algorithm for MADM problems is proposed,which deals with attribute weights and optimal solutions based on preferential evolution metrics.To exemplify applications of the proposed methods,some test functions and,emphatically,controller tuning issues associated with a chemical process are investigated,giving satisfactory results.

Insider Attack Detection Using Deep Belief Neural Network in Cloud Computing

Cloud computing is a high network infrastructure where users,owners,third users,authorized users,and customers can access and store their information quickly.The use of cloud computing has realized the rapid increase of information in every field and the need for a centralized location for processing efficiently.This cloud is nowadays highly affected by internal threats of the user.Sensitive applications such as banking,hospital,and business are more likely affected by real user threats.An intruder is presented as a user and set as a member of the network.After becoming an insider in the network,they will try to attack or steal sensitive data during information sharing or conversation.The major issue in today's technological development is identifying the insider threat in the cloud network.When data are lost,compromising cloud users is difficult.Privacy and security are not ensured,and then,the usage of the cloud is not trusted.Several solutions are available for the external security of the cloud network.However,insider or internal threats need to be addressed.In this research work,we focus on a solution for identifying an insider attack using the artificial intelligence technique.An insider attack is possible by using nodes of weak users’systems.They will log in using a weak user id,connect to a network,and pretend to be a trusted node.Then,they can easily attack and hack information as an insider,and identifying them is very difficult.These types of attacks need intelligent solutions.A machine learning approach is widely used for security issues.To date,the existing lags can classify the attackers accurately.This information hijacking process is very absurd,which motivates young researchers to provide a solution for internal threats.In our proposed work,we track the attackers using a user interaction behavior pattern and deep learning technique.The usage of mouse movements and clicks and keystrokes of the real user is stored in a database.The deep belief neural network is designed using a restricted Boltzmann machine(RBM)so that the layer of RBM communicates with the previous and subsequent layers.The result is evaluated using a Cooja simulator based on the cloud environment.The accuracy and F-measure are highly improved compared with when using the existing long short-term memory and support vector machine.

An information-volume-based distance measure for decision-making

D-S evidence theory,as a general framework for reasoning with uncertainty,allows combining pieces of evidence from different information sources to derive a degree of belief function that is a type of fuzzy measure.However,the mass assignments given by unknown information sources are disordered.How to measure the difference between the mass assignments has aroused people’s interest.In this paper,inspired by the information volume,a novel distance-based measure is proposed to measure the difference between mass assignments.The method can refine the uncertain information given by experts and compare the refined information to obtain the difference between mass assignments.At the same time,it is verified that the measure not only meets the properties of distance,but also proves the superiority of the proposed Information Volume Distance(IVD)through simulation experiments.Meanwhile,in the process of information fusion,the reliability of each source could be quantified through IVD.Therefore,based on IVD,a new multi-source information algorithm is proposed to solve the problem of multi-source information fusion.Moreover,algorithm is applied to decision-making problem and compare with other methods to verify the effectiveness.

COX健康行为互动模式干预对高血压脑出血后偏瘫患者精神状态和健康信念的影响

目的:探讨COX健康行为互动模式干预对高血压脑出血后偏瘫患者精神状态和健康信念的影响。方法:将2018年1月-2022年8月于某院住院治疗的126例高血压脑出血后偏瘫患者依据入院先后顺序分为对照组(n=63)和研究组(n=63)。对照组予以常规健康教育干预,研究组予以COX健康行为互动模式干预。比较两组干预前后的精神状态、健康信念、自我管理能力、日常生活活动能力,并评价两组患者对干预的满意度。结果:干预后,两组汉密尔顿抑郁量表(HAMD)、汉密尔顿焦虑量表(HAMA)评分均低于干预前,且研究组显著低于对照组(t=3.695,2.609;P<0.05);干预后,两组健康信念量表中感知到易感性、感知到严重性、感知到障碍、感知到益处、维护健康动机评分均升高,且研究组各评分显著高于对照组(t=6.150,7.383,3.593,4.922,3.773;P<0.05);干预后,两组自我管理能力各维度评分均升高,且研究组各评分显著高于对照组(t=5.633,4.628,5.705,5.003;P<0.05);干预后3个月,两组日常生活能力量表(ADL)评分和barthel指数评分均升高,且研究组ADL评分、barthel指数评分均显著高于对照组(t=3.772,4.778;P<0.05);研究组对干预的满意度为92.06%,显著高于对照组的74.60%(χ~2=6.914,P<0.05)。结论:对高血压脑出血后偏瘫患者应用COX健康行为互动模式干预能有效改善其精神状态,提升其健康信念及自我管理能力水平,提高患者的日常生活活动能力,且患者满意度高。

蔚县暖泉镇龙王庙的空间布局和民俗信仰及其与泉水、书院的互动

回顾和梳理华北北部地区广泛存在的龙王信仰与祈雨传统,还原蔚县暖泉镇龙王庙的建筑空间及祈雨仪式的具体表现,分析龙王庙庙会在地方文化认同中发挥的作用,深入探讨暖泉镇龙王庙、泉水和王敏书院这三个节点的空间演变所体现出的民俗与文教之间的互动。

基于用户动态交互行为扩展的信念网络推荐模型

针对现有推荐方法证据组合方式单一,未能同时考虑准确性和多样性的问题,提出基于用户动态交互行为扩展的信念网络推荐模型(EBNR_UDIB)。首先,构建一个具有3层结构的基本信念网络推荐(BNR)模型,从而为证据的引入提供一个灵活有效的框架;其次,通过分析用户间的直接及耦合交互关系计算交互强度,并引入动态调整的时间衰减因子修订该强度;最后,以该强度对交互用户加权,将该用户的兴趣作为新证据扩展基本模型,并利用合取和析取两种证据组合方式得到EBNR_UDIB。实验结果表明,相较于基于内容的推荐模型(CBRM)和基于社交的推荐模型(SBRM),在准确率、召回率和F1值上,合取组合方式下的所提模型分别至少提升了7、4和5个百分点,析取组合方式下的所提模型分别至少提升了2、8和6个百分点;在多样性和新颖性指标上,析取组合方式下的所提模型分别至少提升了15和6个百分点,合取组合方式下的所提模型也优于对比模型。

课室融入创新互动教学:教学信念改变与发展

科技的快速发展及日益频繁的全球合作,促成信息获取渠道及沟通模式的巨大改变。因此,调整改进教育的呼吁不断。本研究探讨十二位英语教师(EFL)学习沟通互动教学时的信念改变与发展。这些在职教师在MA硕士学程中修习教学实习课程,并且透过这门课所聚焦的四种创新互动教学,实践基本教学要素,例如以学习者为中心(learner-centeredness)及互动(interaction)。本研究分析的数据包含教师信念报告、互动教学心得、访谈数据,以及教学观察记录。研究结果显示教师教学信念的改变依程度而有所不同,研究结果也显露出可能影响信念发展的成因,以及教师在学习学习的过程中所展现出持续成长的专业能力。

幼儿心理理论的发展与其日常同伴交往关系的研究

通过行为观察（儿童与同伴的一对一游戏）、同伴提名和教师评估，探讨心理理论发展与儿童日常同伴交往之间的关系。52名4，5岁的学前儿童参与了本实验。研究结果表明：（1）4岁以后，儿童的心理理论能力产生了实质性的变化。（2）幼儿心理理论的发展与儿童游戏情境中的同伴交往具有显著的正相关，心理理论发展水平不同的儿童在游戏中的“交往敏感”和“交往表达”两方面具有极其显著的差异。（3）儿童的心理理论水平整体上与同伴提名、教师评估相关不显著，但与儿童的亲社会行为具有紧密的关系。我们推测，儿童心理理论的发展通过影响亲社会行为间接指导儿童日常生活中的同伴交往与同伴关系。

护患互动健康信念模式在预防住院老年病人跌倒中的应用效果

[目的]评价护患互动健康信念模式在预防住院老年病人跌倒中的实施效果。[方法]选取2017年2月—2017年8月广东省人民医院老年医学研究所14个病区收治的302例病人为研究对象,按随机数字表法将病人分为观察组和对照组,各151例。对照组接受基本的预防跌倒措施,观察组在对照组基础上应用护患互动健康信念模式。比较两组入院时、出院时的跌倒效能和观察组老年住院病人预防跌倒质量评价得分。[结果]出院时观察组病人跌倒效能得分高于对照组(P<0.05),观察组预防跌倒质量评价各项指标得分高于入院时(P<0.05)。[结论]护患互动健康信念模式可以降低老年病人跌倒风险,提高老年病人对于防跌倒的信心。

基于区域MRF和贝叶斯置信传播的SAR图像分割

本文通过定义新的势函数,将贝叶斯置信传播算法和区域MRF模型有效结合,提出了一种SAR图像分割算法.考虑到SAR图像丰富的纹理信息,该算法对分水岭分割后的过分割区域提取纹理特征,在得到的区域邻接图上构建MRF模型,并加入区域灰度均值和方差作为区域特征,利用FCM聚类的初分割结果定义区域的关联势函数,并将区域特征引入到置信传播算法中,定义了新的交互势函数.该算法充分利用了SAR图像空间的背景信息,所定义的新的交互势函数能在促进分割结果区域一致性的同时较好保护边缘.实验结果表明,相对于其他MRF模型分割算法,本文算法能取得更好的分割效果.

领导-成员交换与情绪枯竭:互惠信念和权力距离导向的作用

结合社会交换理论和角色理论,领导-成员交换关系中下属的回报不仅受到互惠规范的约束,还受到上下级关系权力不对称的影响。因此,将探索互惠信念和权力距离导向单独及交互项对领导-成员交换与情绪枯竭关系的调节作用。采用两个时间点172名员工的匹配数据,运用层级回归对假设进行了检验。结果表明,领导-成员交换(T1)与情绪枯竭(T2)总体上呈显著负向关系。互惠信念(T1)和权力距离导向(T1)单独对领导-成员交换与情绪枯竭关系的调节作用并不显著,但是二者的交互项的调节作用显著。即,领导-成员交换、互惠信念和权力距离导向对情绪枯竭有显著三维交互作用。具体的,高互惠信念和低权力距离导向的员工,领导-成员交换与情绪枯竭呈显著正向关系;高互惠信念和高权力距离导向的员工,领导-成员交换与情绪枯竭呈显著负向关系。研究验证了领导-成员交换过程中互惠规范的作用,表明互惠规范的适用性受到上下级关系特征的影响。