Theory of wire addition and removal in combinational Boolean networks

Let w_t be a wire in a combinational Boolean network. There may exist a wire w_a such that when w_a is added and w_t is removed, the overall circuit functionality is unchanged. Redundancy-addition-and-removal (RAR) is an efficient technique to find such a w_a. The idea is to add a redundant alternative wire w_a to make the target wire w_t redundant. However, as long as the addition of w_a together with the removal of w_t does not change the overall functionality of the circuit, wires that are added and removed do not necessarily need to be redundant. This raises a question about the existence of alternative wires. Why can one wire replace another wire in a combinational Boolean network? In this paper, we analyze theoretically the existence of alternative wires and model it as an error-cancellation problem. The two existing rewiring techniques, the redundancy-addition-and-removal and the global flow optimization, are unified under the proposed generalized model.     

基于模糊推理的SAR干扰效果评估

对SAR干扰效果的评估是目前的一个研究热点。在依据SAR图像受干扰情况评估SAR干扰效果的过程中,人的主观因素占主导地位。对于这种不确定性的评判,该文介绍了一种基于模糊推理的SAR干扰效果评估方法。首先,建立了SAR干扰效果评估指标;接着,借助相关领域专家的经验知识和推理规则,采用定性与定量相结合的方法,建立了模糊推理数学模型;最后,利用所建立的指标和模型进行干扰效果综合评判,得到了合理的、令人满意的结果。     

基于本体的高校知识分析和推理

以高校知识为研究和应用背景,研究基于本体理论的高校知识本体结构化表示和描述,并采用protege 4.0建立了高校知识本体。同时从高校知识中提取出基本公理、类内公理和类间公理,建立公理库,并采用protege的PAL推理引擎对高校知识进行一致性分析和推理。解决了高校知识语义层次上的信息共享和交互的问题,为高校知识的管理提供语义支撑。     

基于直觉模糊推理的医学图像融合方法研究

针对传统的模糊推理融合方法生成融合图像质量不高的问题,提出了一种基于直觉模糊推理的医学图像融合新方法。根据直觉模糊集的定义,利用直觉模糊理论在处理不确定问题中的优势,制定直觉模糊推理规则,进行图像融合。给出了算法的具体步骤,仿真结果表明,文中提出的算法明显优于传统的模糊推理融合算法。     

一种新的多信息融合算法——比亲算法的提出

根据D-S理论在证据完全对立的情况下失效的原因，考虑证据间基本可信度分配函数(BPA)值在证据中所占比重以及相关程度两个方面对证据合成的影响，在D-S理论框架基础上构建了一种新的多信息融合算法——比亲算法。通过与两篇D-S理论应用论文中的实例进行比较表明：在证据不相对立的情况下，新算法与-S理论应用的结果是一致的；当证据对立时，新算法则具有纠错功能而优于D-S理论。结果验证了这种算法的可行性、有效性及实用性。     

基于模糊推理的多因素传感器管理算法

在信息融合系统中,传感器管理和状态与属性估计、态势及威胁评估等有关。文中将影响传感器管理的目标多因素分为跟踪因素和威胁因素两类,建立了目标跟踪精度高低等级、威胁大小等级与目标对传感器资源需求程度等级之间的模糊规则,以模糊推理计算结果作为传感器资源分配的依据,提出了几种传感器管理算法性能评价指标。仿真结果表明,该算法充分考虑了目标跟踪和威胁等因素,分配结果更加符合战场环境的需要。     

基于字符注意力的自然场景文本识别

自然场景文本识别中采用固定大小的卷积核提取视觉特征,后仅进行字符分类的方法,其全局建模能力弱且忽视了文本语义建模的重要性,因此,本文提出一种基于字符注意力的自然场景文本识别方法。首先构建不同于卷积网络的多级efficient Swin Transformer提取特征,其可使不同窗口的特征进行信息交互;其次设计了字符注意力模块(character attention module, CAM),使网络专注于字符区域的特征,以提取识别度更高的视觉特征;并设计语义推理模块(semantic reasoning module, SRM),根据字符的上下文信息对文本序列进行建模,获得语义特征来纠正不易区分或模糊的字符;最后融合视觉和语义特征,分类得到字符识别结果。实验结果表明,在规则文本数据集IC13上识别准确率达到了95.2%,在不规则的弯曲文本数据集CUTE上达到了85.8%,通过消融及对比实验证明了本文提出的方法可行。     

基于案例推理的黄河流域水资源优化调配

利用水文要素的周期性、重现性、相似性等特征,将嵌套案例推理技术引入到黄河流域水资源调配方案决策中,建立K-D树定性搜索与相似度精确定位相结合的基于案例推理的黄河流域水资源调配决策模型。对黄河流域水资源调配中的水文要素进行定性识别,以关键水文要素的相似度作为检索指标,通过K-D树结构实现快速搜索定位与水文要素的相似性匹配,可搜索出与决策问题高度相似的方案,实现水资源的优化调配。     

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.     

Spontaneous assimilation of continuous values and temporal information in causal induction.

Existing models of causal induction primarily rely on the contingency between the presence and the absence of a causal candidate and an effect. Yet, classification of observations into these four types of covariation data may not be straightforward because (a) most causal candidates, in real life, are continuous with ambiguous, intermediate values and because (b) effects may unfold after some temporal lag, providing ambiguous contingency information. Although past studies suggested various reasons why ambiguous information may not be used during causal induction, the authors examined whether learners spontaneously use ambiguous information through a process called causal assimilation. In particular, the authors examined whether learners willingly place ambiguous observations into one of the categories relevant to the causal hypothesis, in accordance with their current causal beliefs. In Experiment 1, people's frequency estimates of contingency data reflected that information ambiguous along a continuous quantity dimension was spontaneously categorized and assimilated in a causal induction task. This assimilation process was moderated by the strength of the upheld causal hypothesis (Experiment 2), could alter the overall perception of a causal relationship (Experiment 3), and could occur over temporal sequences (Experiment 4). (PsycINFO Database Record (c) 2010 APA, all rights reserved)