Vague Objects Research Articles

Operational shock: A method for estimating cyber security incident costs for large Australian healthcare providers

ABSTRACT This paper introduces a novel cyber incident cost estimation methodology, applicable to large Australian healthcare providers. A review demonstrates the poor utility of current risk estimation approaches and the vulnerability of healthcare networks is evaluated using Leibniz’s law of indiscernibles, and Evans’ theory of vague objects. Finally, a quantitative cost calculation method is proposed, merging temporal and impact variables with service data from the Australian Institute of Health and Welfare. This research demonstrates that existing attempts to measure cyber incident risk produces vague results. This is evidenced by 929 Australian healthcare data breaches recorded over 5 years, a AU$0.6bn annual national risk exposure, and low levels of healthcare cyber maturity across three states. The likelihood of data breaches is reported as 99.4%, with known ICT vulnerabilities exceeding 207,000. After logically concluding that healthcare networks are fundamentally insecure, an ‘operational shock’ calculation method is modelled against the AIHW data, to illustrate realistic cyber incident costs. This returns an exposure across Australia’s acute care hospital network of AU$148.1 m from a single incident that takes 1 week to resolve. In considering this quantum, risk transfer options using cyber insurance and improved agency cyber risk programs are required to mitigate significant financial risks.

Boosting R-CNN: Reweighting R-CNN samples by RPN’s error for underwater object detection

Complicated underwater environments bring new challenges to object detection, such as unbalanced light conditions, low contrast, occlusion, and mimicry of aquatic organisms. Under these circumstances, the objects captured by the underwater camera will become vague, and the generic detectors often fail on these vague objects. This work aims to solve the problem from two perspectives: uncertainty modeling and hard example mining. We propose a two-stage underwater detector named boosting R-CNN, which comprises three key components. First, a new region proposal network named RetinaRPN is proposed, which provides high-quality proposals and considers objectness and IoU prediction for uncertainty to model the object prior probability. Second, the probabilistic inference pipeline is introduced to combine the first-stage prior uncertainty and the second-stage classification score to model the final detection score. Finally, we propose a new hard example mining method named boosting reweighting. Specifically, when the region proposal network miscalculates the object prior probability for a sample, boosting reweighting will increase the classification loss of the sample in the R-CNN head during training, while reducing the loss of easy samples with accurately estimated priors. Thus, a robust detection head in the second stage can be obtained. During the inference stage, the R-CNN has the capability to rectify the error of the first stage to improve the performance. Comprehensive experiments on two underwater datasets and two generic object detection datasets demonstrate the effectiveness and robustness of our method. The link of code:https://github.com/mousecpn/Boosting-R-CNN.

Picture Fuzzy Relations over Picture Fuzzy Sets

Nowadays, picture fuzzy set theory is a flourishing field in mathematics with uncertainty by incorporating the concept of positive, negative and neutral membership degrees of an object. A traditional crisp relation represents the satisfaction or the dissatisfaction of relationship, connection or correspondence between the objects of two or more sets. However, there are some problems that can’t be solved through classical relationships, such as the relationship between two objects being vague. In those situations, picture fuzzy relation over picture fuzzy sets is an important and powerful concept which is suitable for describing correspondences between two vague objects. It represents the strength of association of the elements of picture fuzzy sets. It plays an important role in picture fuzzy modeling, inference and control system and also has important applications in relational databases, approximate reasoning, preference modeling, medical diagnosis, etc. In this article, we define picture fuzzy relations over picture fuzzy sets, including some other fundamental definitions with illustrations. The max-min and min-max compositions of picture fuzzy relations are defined in the light of picture fuzzy sets and discussed some properties related to them. The reflexivity, symmetry and transitivity of a picture fuzzy relation are described over a picture fuzzy set. Finally, various properties are explored related to the picture fuzzy relations over a picture fuzzy set.

Content Consistency Preserving Style Transfer Network

For the problem that it will generate errors in the process of content feature transfer and cross-domain feature fusion caused by encoder-decoder style transfer networks, a content consistency preserving style transfer network is proposed. Source and target images are input to the encoding module for extracting content and style features respectively. Dual-chain feature transfer module constructs the same depth feature mapping with content encoder and decoder, and processes content feature parallelly by feature enhancement and feed-forward reference chains. Content and style features are fused in decoder and output style transfer result. With the simulation test based on multimodal unsupervised image-to-image translation (MUNIT), the style transfer effect of proposed network on vague objects and low contrast between the objects and background in low light environment is significantly increased. The experiment results on BDD100K dataset show that FID and IS are decreased by 3.2% and increased by 8.6% on average than MUNIT respectively, the proposed network can achieve content consistently and style accurately image trans-formation, which is able to application autonomous vehicle systems.

Decision-making methods based on fuzzy soft competition hypergraphs

Fuzzy soft set theory is an effective framework that is utilized to determine the uncertainty and plays a major role to identify vague objects in a parametric manner. The existing methods to discuss the competitive relations among objects have some limitations due to the existence of different types of uncertainties in a single mathematical structure. In this research article, we define a novel framework of fuzzy soft hypergraphs that export the qualities of fuzzy soft sets to hypergraphs. The effectiveness of competition methods is enhanced with the novel notion of fuzzy soft competition hypergraphs. We study certain types of fuzzy soft competition hypergraphs to illustrate different relations in a directed fuzzy soft network using the concepts of height, depth, union, and intersection simultaneously. We introduce the notions of fuzzy soft k-competition hypergraphs and fuzzy soft neighborhood hypergraphs. We design certain algorithms to compute the strength of competition in fuzzy soft directed graphs that reduce the calculation complexity of existing fuzzy-based non-parameterized models. We analyze the significance of our proposed theory with a decision-making problem. Finally, we present graphical, numerical, as well as theoretical comparison analysis with existing methods that endorse the applicability and advantages of our proposed approach.

Evans' Argument and Vague Objects

In 1978, Gareth Evans published a short and somewhat cryptic
 article purporting to establish that there are no vague objects. This
 paper is a commentary on this. Prima facie, the claim that there
 are no vague objects is clearly false. Mt Everest, for example, has no
 precise boundaries. And if this is so, there must be something wrong
 with Evans' argument. In the paper, I discuss what this is, giving a
 model of vague objects in the process.

Pyramid Frequency Feature Fusion Object Detection Networks

<p indent=0mm>For the problem of the absence of detail texture and other high-frequency features in the feature extraction process of deep learning network employing the up-sampling operation, a pyramid frequency feature fusion object detection network is proposed with three deep learning pyramid networks, to balance the high and low frequency feature information and improve the detection accuracy. The deep feature of the input image is extracted from the primary pyramid, different frequency characteristics are formed respectively by the high and low frequencies enhancement pyramid. In the process of information transmission, feature fusion is used to highlight the detail information protection ability of deep learning network and improve the object detection capability. After the simulation test based on CornerNet algorithm framework, the detection effect of proposed algorithm on vague objects, overlapping objects and low contrast between the objects and the background is significantly increased. The detection results on COCO dataset are more than 1% higher than CornerNet algorithm, so the proposed algorithm has a good performance in detecting pedestrians, vehicles and other objects, which is able to application autonomous vehicle systems and smart robots.

On the Alleged Inconsistency in Van Inwagen’s Rebuttal of Evans’ Argument

The paper attempts to interpret P. van Inwagen’s refutation of Evans’ argument that there cannot be vague objects and defend it against the charge of inconsistency raised by Radim Bělohrad. However, such an interpretation is not without a cost. Therefore another interpretation of van Inwagen’s example of the Cabinet is offered which evades Evans’ charge of inconsistency against indeterminate identity as it does not need the notion at all.

Spots Concept for Problems of Artificial Intelligence and Algorithms of Neuromorphic Systems

The paper presents the concept and basis for the apparatus of a new mathematical object—spots, which follows the concept of “mental images” in psychology and corresponds to the idea of vague geometric objects. Since mental images are semantic ones, spots appear to be an adequate mathematical object, promising for solving problems of artificial intelligence (AI), including the knowledge representation and modeling of the processes of human reasoning. A possible new architecture of constructing neural networks using the introduced elementary relations of spots, as well as application of vectors and matrices in which the elementary relations of spots play the role of numerical values, are considered. Variants of the implementation of a new mathematical model at the hardware level for creating neuromorphic systems based on new promising components of memristors and FeFETs, which have non-volatile memory, extremely low switching losses and faster read/write operations, are also proposed. The suggested concept seems to be important both in the theoretical aspect, as section of the Qualitative Mathematics, and for its application in many areas of AI.

Modeling the Vagueness of Areal Geographic Objects: A Categorization System

Modeling vague objects with indeterminate boundaries has drawn much attention in geographic information science. Because fields and objects are two perspectives in modeling geographic phenomena, this paper investigates the characteristics of vague regions from the perspective of the field/object dichotomy. Based on the assumption that a vague object can be viewed as the conceptualization of a field, we defined five categories of vague objects: direct field-cutting objects, focal operation-based field-cutting objects, element-clustering objects, object-referenced objects, and dynamic boundary objects. We then established a categorization system to formalize the semantic differences between vague objects using the fuzzy set theory. The proposed framework provides valuable input for the conceptualization, interpretation, and modeling of vague geographical objects.

Metaphysical Vagueness Without Vague Objects

Elizabeth Barnes and Robert Williams have developed a theory of metaphysical indeterminacy, via which they defend the theoretical legitimacy of vague objects. In this paper, we argue that while the Barnes–Williams theory supplies a viable account of genuine metaphysical vagueness, it cannot underwrite an account of genuinely vague objects. First we clarify the distinction between these two key theses. Then we argue that the Barnes–Williams theory of metaphysical vagueness not only fails to deliver genuinely vague objects, it in fact provides grounds for rejecting them.

Vague objects and vague identity: new essays on ontic vagueness

Vague objects and vague identity: new essays on ontic vagueness

Vague Objects within Classical Logic and Standard Mereology, and without Indeterminate Identity

Weatherson (The Philosophical Quarterly, 53, 481–501 2003) argues that whoever accepts classical logic, standard mereology and the difference between vague objects and any others, should conclude that there are no vague objects. Barnes and Williams (Pacific Philosophical Quarterly, 90, 176–187 2009) claim that a supporter of vague objects who accepts classical logic and standard mereology should recognize that the existence of vague objects implies indeterminate identity. Even though it is not clearly stated, they all seem to be committed to the assumption that reality is ultimately constituted by mereological atoms. This assumption is not granted by standard mereology which instead remains silent on whether reality is atomic or gunky; therefore, I contend that whoever maintains classical logic, standard mereology and the difference between vague objects and any others, is not forced to conclude with Weatherson that there are no vague objects; nor is she compelled to revise her point of view according to Barnes and Williams’s proposal and to accept that the existence of vague objects implies indeterminate identity.

Multiple reference and vague objects

Kilimanjaro is an example of what some philosophers would call a ‘vague object’: it is only roughly 5895 m tall, its weight is not precise and its boundaries are fuzzy because some particles are neither determinately part of it nor determinately not part of it. It has been suggested that this vagueness arises as a result of semantic indecision: it is because we didn’t make up our mind what the expression “Kilimanjaro” applies to that we can truthfully say such things as “It is indeterminate whether this particle is part of Kilimanjaro”. After reviewing some of the limitations of this approach, I will propose an alternative account, based on a new semantic relation—multiple reference—capable of holding in a one-many pattern between a term and several objects in the domain. I will explain how multiple reference works, what differentiates it from plural reference and how it might be used to accommodate at least some aspects of our ordinary discourse about vague objects.

Fuzzy sb*-Closed Sets

The fuzzy topological concepts enrich information in classifying vague objects, error control, calculating fuzzy topological relations etc. In this article, we studied the new form of vague subset called fuzzy sb*-open set and fuzzy sb*-continuous functions in theoretical point of view. The generalization of these concepts can link the fuzzy sets with the modeled real world entities.

Referential Indeterminacy with an Ontic Source? – A Criticism of Williams’s Defense of Vague Objects

Abstract J. Robert G. Williams argues that referential indeterminacy may arise as a result of ontic indeterminacy, and that lambda-abstraction is not applicable to indeterminate identity statements, i.e., statements of the form, ‘it is indeterminate whether a $$a$$ is identical with b $$b$$ ,’ if they involve such ontically induced referential indeterminacy; so Evans’s r e d u c t i o $$reductio$$ argument against vague objects does not work. This paper finds fault with Williams’s defense of vague objects and shows that there is much reason to doubt that the idea of ontically induced referential indeterminacy plays any significant role in a proper defense of vague objects against Evans’s argument.

The existence of vague objects

We consider a category whose objects are defined as triplets (S,e,c̲) where S is a set, e is a similarity and c̲∈S. An object is called crisp if the similarity is crisp, vague otherwise. We indicate different ways of obtaining a similarity and therefore different ways to obtain an object. The paper is mathematical in nature, nevertheless I hope that the proposed formalisms are in some ways connected with the very interesting ontological question about the existence of vague objects in real world.

Indeterminacy and ritual symbol. Philosophical remarks on Ernesto De Martino’s The Land of Remorse

This article analyses the Italian philosopher and anthropologist Ernesto De Martino’s The Land of Remorse from a philosophical viewpoint. After having presented the main Demartinian concepts (e.g. ‘presence’ and ‘crisis of presence’) and examined the phenomenon of ‘tarantism’ (that is a magical-religious ritual practiced in southern Italy), the author interprets ‘ritual symbols’ as useful ‘fictions,’ which permit to resolve the problem of ‘indeterminacy’ (that refers to vague objects and unknown events), and rescue the human Self from psychological and existential crisis.

Vague Objects with Sharp Boundaries

AbstractIn this article I shall consider two seemingly contradictory claims: first, the claim that everybody who thinks that there are ordinary objects has to accept that they are vague, and second, the claim that everybody has to accept the existence of sharp boundaries to ordinary objects. The purpose of this article is of course not to defend a contradiction. Indeed, there is no contradiction because the two claims do not concern the same ‘everybody’. The first claim, that all ordinary objects are vague, is a claim that stems both from common sense intuitions as well as from various types of ontologies of ordinary objects. This puts then pressure on theories of vagueness to account for the vague nature of ordinary objects – but, as we shall see, all theories of vagueness have to accept the existence of sharp thresholds. This is obvious in the case of epistemicism, and it is a well‐known defect of supervaluationism, but as we will see friends of metaphysical vagueness do have to endorse the existence of sharp thresholds in their theory as well. Consequently, there are reasons for dissatisfaction with these accounts, since they do not seem to be able to do the job we asked them to do.

Vague Objects and the Problem of the Many

The problem of the many poses the task of explaining mereological indeterminacy of ordinary objects in a way that sustains our familiar practice of counting these objects. The aim of this essay is to develop a solution to the problem of the many that is based on an account of mereological indeterminacy as having its source in how ordinary objects are, independently of how we represent them. At the center of the account stands a quasi-hylomorphic ontology of ordinary objects as material objects with multiple individual forms.