Critical Relation Path Aggregation-Based Industrial Control Component Exploitable Vulnerability Reasoning

With the growing discovery of exposed vulnerabilities in the Industrial Control Components(ICCs),identification of the exploitable ones is urgent for Industrial Control System(ICS)administrators to proactively forecast potential threats.However,it is not a trivial task due to the complexity of the multi-source heterogeneous data and the lack of automatic analysis methods.To address these challenges,we propose an exploitability reasoning method based on the ICC-Vulnerability Knowledge Graph(KG)in which relation paths contain abundant potential evidence to support the reasoning.The reasoning task in this work refers to determining whether a specific relation is valid between an attacker entity and a possible exploitable vulnerability entity with the help of a collective of the critical paths.The proposed method consists of three primary building blocks:KG construction,relation path representation,and query relation reasoning.A security-oriented ontology combines exploit modeling,which provides a guideline for the integration of the scattered knowledge while constructing the KG.We emphasize the role of the aggregation of the attention mechanism in representation learning and ultimate reasoning.In order to acquire a high-quality representation,the entity and relation embeddings take advantage of their local structure and related semantics.Some critical paths are assigned corresponding attentive weights and then they are aggregated for the determination of the query relation validity.In particular,similarity calculation is introduced into a critical path selection algorithm,which improves search and reasoning performance.Meanwhile,the proposed algorithm avoids redundant paths between the given pairs of entities.Experimental results show that the proposed method outperforms the state-of-the-art ones in the aspects of embedding quality and query relation reasoning accuracy.

RESEARCH INTO DEVELOPING INTELLIGENT CAE SYSTEMS——An FEM-based Mechanism Design Approach

A general methodology for developing intelligent CAE systems is presented, which is based on a so-called Model Based Reasoning Approach (MBRA) in knowledge engineering. Generic features of CAE systems are described, and a generic model for the architecture of CAE systems with these generic features is then presented. A model-based reasoning approach is modified for implementing the generic model. A case for developing a computer system for the mechanism design based on a specific finite element theory is used to explain the methodology.

A Novel Mechanism in MPLS Network under Adversarial Uncertainty

The advantage of multi-protocol label switching (MPLS) is its capability to route the packets through explicit paths. But the nodes in the paths may be possibly attacked by the adversarial uncertainty. Aiming at this problem in MPLS Network, in this paper, we propose a novel mechanism in MPLS network under adversar-ial uncertainty, making use of the theory of artificial intelligence, at first, we find the initialized label switching paths (LSPs) using the A* arithmetic, and secondly, during the process of data transmission, we switch the transmission path duly by taking advantage of the non-monotone reasoning mechanism. Com-pared to the traditional route mechanism, the experimental results show that it improves the security if data transmission remarkably under our novel mechanism in MPLS network.

A MACHINE LEARNING APPROACH FOR MECHANISM SELECTION IN COMPLEX NEGOTIATIONS

Automated negotiation mechanisms can be helpful in contexts where users want to reach mutually satisfactory agreements about issues of shared interest, especially for complex problems with many interdependent issues. A variety of automated negotiation mechanisms have been proposed in the literature. The effectiveness of those mechanisms, however, may depend on the charaeteristics of the underlying negotiation problem （e.g. on the complexity of participant＇s utility functions, as well as the degree of conflict between participants）. While one mechanism may be a good choice for a negotiation problem, it may be a poor choice for another. In this paper, we pursue the problem of selecting the most effective negotiation mechanism given a particular problem by （1） defining a set of scenario metrics to capture the relevant features of negotiation problems, （2） evaluating the performance of a range of negotiation mechanisms on a diverse test suite of negotiation scenarios, （3） applying machine learning techniques to identify which mechanisms work best with which scenarios, and （4） demonstrating that using these classification rules for mechanism selection enables significantly better negotiation performance than any single mechanism alone.

Payments for watershed ecosystem services:mechanism,progress and challenges

Introduction:Payment for watershed ecosystem services(PWES),a policy instrument for compensating for the externality of watershed ecosystem/environmental services,has gained in policy importance in China over the past two decades.Many scholars and researchers have contributed to the conceptualization of this policy framework by developing operational mechanisms as well as compensation standards for PWES.Outcomes:This article reviews 27 PWES schemes piloted in China and in 10 other countries,with a particular emphasis on successful cases of land-use conversion programs,such as the Paddy Land to Dry Land Program and Sloping Land Conversion Program that have been implemented in China.Discussion:By comparing different cases,the authors attempt to answer the following questions:what were the ecological and institutional contexts in which these schemes were established and how did they work?What were the actual efficiencies and impacts of these piloted schemes?Which scheme worked better in certain ecological,socio-economic,and institutional contexts?Conclusion:Based on case studies,the authors draw the following conclusions about Chinese PWES:(1)to establish an acceptable standard for a PWES program,it is necessary to estimate the economic and social costs regarding the livelihoods of households;(2)multistakeholder negotiation mechanism for PWES,including intermediaries,such as the local government,NGO/NPOs,village committees,and user associations,should be used;(3)ES,non-market services,should acquire positive externalities to accomplish an optimal win–win pattern concerning both environmental goals and the livelihoods of local resource users.

基于时序知识推理的时序知识图谱补全方法

基于知识推理的知识图谱补全技术研究在静态图谱上已经获得了较为明显的效果,但其在处理与时间相关的事件上仍存在着不足,而基于时序推理的知识图谱补全方法更加贴合真实事件,有较高的研究价值。然而,现有的时序知识图谱补全技术在处理节点信息和全局信息上存在局限性问题。因此,提出了一种基于注意力聚合邻居信息并使用双向LSTM处理时间信息的改进方法。首先,通过推理预测的方式补全时序知识图谱中缺失的信息,并在推理过程中生成推理路径图来解决由神经网络所带来的不可解释性问题;然后,使用4种不同时间跨度的公开数据集进行了实验并与主流方法进行了比较。实验结果表明:所提方法在R mr、h@1和h@10这3个指标上是优于现有方法的。

情理融入司法裁判的逻辑机理与现实路径

将“情理”融入司法裁判说理是当代法治中国建设的必然要求。在当前的司法实践中,“情理”融入司法裁判存在标准不统一、定位不准确、论证不科学和阐释不合理等问题。针对存在问题,宏观方面应从完善法律法规、统一适用标准、准确定位关系和推行司法公开入手推进司法体制改革,微观层面裁判者要从法律证成、法律解释和法律修辞等方面改进融入方法,实现“看得见的正义”向“说得出的正义”转变。

甜加糯型鲜食玉米留层加工关键工艺研究

为了最大程度延长并保持甜加糯型鲜食玉米的营养成分和口感,对玉米加工工艺进行研究。首先,确定了需要保留玉米内部2~4片苞叶的加工要求;其次,以冀糯173甜加糯型鲜食玉米为研究对象,掌握了鲜食玉米苞叶包覆状态及其在穗柄上的生长分布规律,提出了先定位切割再利用内外层苞叶附着力的差异去除外层苞叶的加工工艺;然后,在开展鲜食玉米外部形态尺寸测量实验的基础上,设计了一种环状定位结构,并搭建了去苞叶设备;最后,通过合理留层试验对所提出的加工工艺进行了验证。结果表明:先经定位环定位切割穗柄,再经去苞叶设备处理的玉米能够苞叶合理留层2~4片,定位环参数为大内径53 mm、小内径41 mm、接触区8 mm、保护区2 mm、厚度2.5 mm。先定位切割再去除外层苞叶的加工工艺合理可行,能够实现甜加糯型鲜食玉米合理保留内层苞叶的加工目标。鲜食玉米留层加工工艺可为甜加糯型鲜食玉米留层加工生产线的设计和改进提供参考。

政府治理创新促进国家认同的逻辑理路研究

当前,世界百年未有之大变局加速演进,政府治理创新成为各国推动社会进步和国家发展的重要驱动力。政府治理创新与国家认同之间双向耦合驱动,两者相辅相成,共同推进国家的发展进步。分析政府治理创新与国家认同二者之间的逻辑理路可以发现,我国政府治理创新面临着信息透明度、文化多样性、经济发展不均、数字技术变革等多重治理挑战。党的二十届三中全会进一步明确继续完善和发展中国特色社会主义制度,推进国家治理体系和治理能力现代化的方向。政府治理现代化是国家治理体系和治理能力现代化的重要内容,而政府治理创新是衡量其成效的重要维度,新时代的政府治理创新驱动机制多元,涵盖政策和法规激励驱动、经济和财务激励驱动、文化和教育推广驱动等方面,这些机制的相互作用,构成了促进国家认同的动力系统,有助于国家认同的建设与巩固。通过整合这些创新机制,政府不仅可以有效应对当下的治理挑战,还能为国家的可持续发展奠定坚实基础。政府治理创新与国家认同之间的互动关系,既反映了我国在应对内外挑战时的战略思考,也为推动国家治理现代化提供了重要路径,通过不断探索与实践,深化治理创新,将为实现更高水平的国家认同和社会和谐奠定基础。

An asymptotically optimal public parking lot location algorithm based on intuitive reasoning

In order to solve the problems of road traffic congestion and the increasing parking time caused by the imbalance of parking lot supply and demand,this paper proposes an asymptotically optimal public parking lot location algorithm based on intuitive reasoning to optimize the parking lot location problem.Guided by the idea of intuitive reasoning,we use walking distance as indicator to measure the variability among location data and build a combinatorial optimization model aimed at guiding search decisions in the solution space of complex problems to find optimal solutions.First,Selective Attention Mechanism(SAM)is introduced to reduce the search space by adaptively focusing on the important information in the features.Then,Quantum Annealing(QA)algorithm with quantum tunneling effect is used to jump out of the local extremum in the search space with high probability and further approach the global optimal solution.Experiments on the parking lot location dataset in Luohu District,Shenzhen,show that the proposed method has improved the accuracy and running speed of the solution,and the asymptotic optimality of the algorithm and its effectiveness in solving the public parking lot location problem are verified.

裁判文书不说理的制度逻辑

裁判文书说理问题关系到人民群众能否在每一个司法案件中感受到公平正义。处于法院系统中的法官的行动面临多重工作要求和制度约束,裁判文书说理的制度性要求并非一直“在场”,而且“在场”的形式和定位也不是始终不变。裁判文书说理问题不只是一个技术,或者说个人素质和技巧问题,因为受制于外在的制度环境和社会结构,法官并不能随心所欲地安排自己的时间和精力,其中蕴含着一种注意力分配机制。这种注意力分配机制具有理性选择的能动性基础,但也受到社会身份、目标和认知等方面的影响。制度性的约束需要通过一种激励制度实现,从而影响法官的注意力分配,激励法官积极实施制度要求的事情。裁判文书不说理问题在激励制度上的根源可以从显性激励机制、隐性激励机制和监督激励机制等方面被审视。改变裁判文书不说理的现状,不能仅仅落入法官个人素质的窠臼,而是需要从制度体系产生的正向和反向激励入手进行改革与完善。

已公开个人信息合理处理的实践困境及因应之策

近年来,我国陆续出台相关法律法规,逐步完善了个人信息处理规范,但直到《民法典》的出台,已公开个人信息才被作为个人信息的一种特别类目进行特殊保护。不同于一般个人信息处理中的“告知-同意”规则,已公开个人信息处理规则的特殊性体现在个人信息处理者可以在合理范围内对其进行处理,而无需个人信息所有者的事前同意。但目前已公开个人信息处理实践面临困境:以必要性原则为代表的处理原则界定模糊;“选择-退出”机制与“删除权”机制的实现存在障碍;自由裁量权行使不一的情况时有出现。因此,扫除在个人信息保护深入推进道路上埋下的隐患刻不容缓。文章结合我国现有判例,参考域外相关立法与理论,提出已公开个人信息合理处理的因应之策:在规范层面,对必要性原则进行强化以及将动态系统论引入处理合理性的判断;在机制层面,完善“选择-退出”机制与“删除权”机制。

融合特征分析及机器学习的可演进变压器故障诊断模型

变压器是大型电力系统中的关键重要设备,其机理复杂且影响面广,对变压器的状态检测与故障诊断是传统电力系统中的关键问题,也是智能化时代下智能算法应用的重要方向。为解决现有的智能化故障诊断研究受限于故障样本稀缺、诊断结果可解释性差、模型更新困难等问题,提出一种基于时序流数据动态分析的变压器故障诊断模型。首先通过人工辅助标注和数据增强方法,构建具有序列特征的高置信故障数据样本库;然后使用由融合时序特征分析器和多分类器构成的神经网络模型作为训练及分析的模型基础,构造基于相似案例的推理方式,通过距离相似性、模式相似性、形状相似性等多维度距离度量方法,对实时检测到的流数据进行故障诊断及分类预警,以指导运维人员结合历史经验及智能技术开展故障分析。实验结果表明,所提方法在故障诊断的准确性与可解释性上显著提升,可应用于变压器故障在线诊断真实场景中。

基于动态常识推理与多维语义特征的幽默识别

随着社交媒体的飞速发展,幽默识别任务受到研究者广泛关注。其目标是判断给定文本是否具有幽默表达。现有方法主要基于幽默产生理论,采用规则或神经网络模型提取多种幽默相关特征,如不一致性、情感和语音等。然而,这些方法未充分捕捉文本内部的情感特征,忽视了隐含在幽默文本中的情感表达,影响了幽默识别的准确性。为解决此问题,该文提出了CMSOR方法,以动态常识和多维语义特征为驱动。首先,利用外部常识信息从文本中动态推理说话者的隐式情感表达;然后,引入WordNet词典计算词级语义距离,捕捉不一致性,并计算模糊性特征;最后,基于这三个特征维度构建幽默语义,实现幽默识别。实验证明,CMSOR模型相对于当前基准模型在三个公开数据集上的识别性能均有显著提升。

灯影组二段底水气藏试采特征及气井合理配产研究——以安岳气田GS1井区为例

四川盆地安岳气田GS1井区灯影组二段气藏为大型低幅宽缓构造、下部发育具统一气水界面底水的裂缝—孔洞型气藏,非均质性较强,开发过程中面临底水沿高角度裂缝、孔洞推进风险。为了提高GS1井区灯影组二段底水气藏开发效果,文章通过气藏特征综合研究,储层渗流特征、井区试采动态特征总结,综合考虑储层展布、气水关系将气藏划分四类水侵风险区,建立水侵机理模型并进行数值模拟,预测区域水侵模式。结合国内外典型缝洞型底水气藏研究,设计GS1井区底水气藏采气速度合理区间1.5%~3%,以延长无水采气期和确保长期稳产为目的,制定四类水侵风险区气井配产,有效指导气藏开发生产并获得了阶段性成效,为同类裂缝—孔洞型底水气藏的开发生产提供了有益指导。

融合HousE和注意力机制的知识推理模型

知识推理技术是解决知识图谱缺失问题所提出的方法,并在近年来不断发展。为了解决推理中准确度低、可解释性差、适用性不强等问题,提出了一种融合注意力机制和HousE的知识推理模型Att-HousE。该模型由一个带注意力机制的规则生成器和一个带HousE嵌入的规则预测器组成,规则生成器生成推理需要的规则并传入预测器,预测器更新并得到不同规则的得分,然后通过EM算法不断训练优化生成器与预测器。具体而言,该模型是建立在RNNLogic的基础上并作出改进,注意力机制可以选取更值得关注的关系作为规则,提高了模型准确度,HousE嵌入则在处理复杂关系上更具有灵活性,并适用于建立多边关系。在公开实验数据集上的结果表明,Att-HousE在FB15K-237上做推理任务时,MRR指标整体比RNNLogic高出6.3%;在稀疏数据集WN18RR上,Hits@10指标整体比RNNLogic高出2.7%,证明了引入HousE和注意力机制后可以更全面地抓取和形成多边关系,提升知识推理的精度。

日韩医保支付标准调整方式对我国谈判药品简易续约规则的启示

目的 借鉴日本和韩国医保支付标准调整的实践经验,为我国简易续约规则的完善提供参考。方法 检索中国知网的相关文献、日本和韩国政府官网的相关政策文件等,从调整条件和调整公式两个方面归纳总结目前日韩调整医保支付标准的做法,并与我国现行简易续约规则进行对比,明晰我国简易续约规则可优化之处,提出政策建议。结果与结论 在调整方式上日韩与我国相似,对于超量药品根据超量情况计算药品降幅并实施调整;但具体的调整条件和调整公式又有所不同,日韩针对目前超量较大的药品采取线性降价的方式,而我国针对目前和预期均超量较大的药品采取梯度降价的方式。对比分析结果显示,我国已初步建立了符合国情和医保实际的简易续约规则,并采取了一些创新举措,包括考量药品目前与预期的超量情况和在调整公式中引入降幅减半机制;但同时也存在一定不足,如调整条件的指标设置较为单一、调整公式的梯度降价区间过于宽泛,未能充分体现“以量换价”的市场化机制。建议我国医保部门在简易续约时增加对药品基金支出大小的考量、细化调整公式的梯度降价区间、加大对特殊品类药品在新增适应证时的政策倾斜,进一步完善简易续约规则。

基于历史学习和关系注意力的时序知识图谱推理

时序知识图谱在传统静态知识图谱上进一步引入了时间维度,由此引出时序知识图谱推理任务,旨在推理未来事件或缺失事实的实体或关系。针对大多数时序知识图谱推理模型存在没有充分利用历史事实和关系关联分析的问题,该文提出一种基于历史学习和关系注意力的时序知识图谱推理方法(简称HLRA)。为了充分利用历史事实,使用多层感知机学习历史事实中的时间戳权重,并结合复制模式的思想,编码出具备时间权重的历史语义偏移向量,在此基础上关联查询信息得到历史学习评分。另一方面,使用自注意力机制分析关系间的关联,将计算出的关系间注意力分数作为影响因子,并将之加权到实体预测中得到关系注意力的得分。最终,通过结合两个分数以获得综合的置信分数。在ICEWS18、ICEWS14、YAGO和GDELT等数据集上的实验结果表明,HLRA模型在MRR、Hits@1、Hits@3和Hits@10等评价指标上较次优模型获得1%至4%的提升,有效提升了时序图谱推理的能力,是一种效果更好的模型。

创新药价格形成与医保准入的影响因素分析

在创新药新上市即将面临医保国谈的背景下,哪些因素可能影响创新药的价格形成和医保准入,成为决策者和社会各界广泛关注的问题。本研究从创新药价格形成的经济学原理、医保谈判的中国实践、医保准入的不同模式、现有研究的主要发现四个方面进行分析。研究发现,从微观上,价值和成本是创新药价格的上下限,临床价值是最重要的价值维度,需要持续评估。从宏观上,国家经济发展水平对支付方和供给侧都会带来复杂和深远的影响。医保基于“战略购买”思路,需要遴选“性价比”更高的药品,药物经济学评价是必要的工具之一,其有助于降低决策风险,促进“有效市场”和“有为政府”更好发挥作用。