A socio-cognitive and computational model for decision making and user modelling in social phishing

Systems software quality, and system security in particular, is often compromised by phishing attacks. The latter were relatively easy to detect through phishing content filters, in the past. However, it has been increasingly difficult to stop more recent and sophisticated social phishing attacks. To protect the citizens from new types of phishing attacks, software quality engineers need to provide equally sophisticating preventive technology that models people’s reactions. The authors considered the behaviour of people on the Internet from a socio-cognitive perspective and deduced who could be more prone to be spoofed by social phishing techniques. The authors herein propose a computational and interdisciplinary metamodelling methodology, which can assist in capturing and understanding people’s interactive behaviour when they are online. Online behaviour can reveal Internet users’ knowledge, information, and beliefs in a given social context; these could also constitute significant factors for trust in social phishing circumstances which, in turn, can provide valuable insights and decision making meta-knowledge for recognition of potential victims of phishers. The proposed modelling approach is illustrated and explained using real-life phishing cases. This meta-model can i) help social computing and phishing researchers to understand users’ trust decisions from a socio-cognitive perspective, and ii) open ways to integrate artificial intelligence design techniques within software quality management practices in order to protect citizens from being spoofed by social phishing attacks. Thus, this software design quality approach will increase system security as a proactive maintenance strategy

Distributed Extended Object Tracking Using Coupled Velocity Model from WLS Perspective

This study proposes a coupled velocity model (CVM) that establishes the relation between the orientation and velocity using their correlation, avoiding that the existing extended object tracking (EOT) models treat them as two independent quantities. As a result, CVM detects the mismatch between the prior dynamic model and actual motion pattern to correct the filtering gain, and simultaneously becomes a nonlinear and state-coupled model with multiplicative noise. The study considers CVM to design a feasible distributed weighted least squares (WLS) filter. The WLS criterion requires a linear state-space model containing only additive noise about the estimated state. To meet the requirement, we derive such two separate pseudo-linearized models by using the first-order Taylor series expansion. The separation is merely in form, and the estimates of interested states are embedded as parameters into each other's model, which implies that their interdependency is still preserved in the iterative operation of two linear filters. With the two models, we first propose a centralized WLS filter by converting the measurements from all nodes into a summation form. Then, a distributed consensus scheme, which directly performs an inner iteration on the priors across different nodes, is proposed to incorporate the cross-covariances between nodes. Under the consensus scheme, a distributed WLS filter over a realistic network with ``naive'' node is developed by proper weighting of the priors and measurements. Finally, the performance of proposed filters in terms of accuracy, robustness, and consistency is testified under different prior situations.Comment: Corrected Versio

Index relations and fusion rules: Explorations of Supersymmetric, Conformal, and Topological Field Theories.

PhD Theses.This thesis explores the world of quantum eld theories through an analytic approach. It focuses on three special types of quantum eld theories: supersymmetric, conformal and topological ones. The necessary background knowledge is introduced in chapter one, then two types of problems are studied in the next three chapters: index relations and fusion rules.1 For index relations we study certain exactly marginal gaugings involving arbitrary numbers of Argyres-Douglas (AD) theories and show that the resulting Schur indices are related to those of certain Lagrangian theories of class S via simple transformations. By writing these quantities in the language of 2D topological quantum eld theory (TQFT), we easily read o the S-duality action on the avor symmetries of the AD quivers and also nd expressions for the Schur indices of various classes of exotic AD theories appearing in di erent decoupling limits. The TQFT expressions for these latter theories are related by simple transformations to the corresponding quantities for certain well-known isolated theories with regular punctures (e.g., the Minahan-Nemeschansky E6 theory and various generalizations). We then reinterpret the TQFT expressions for the indices of our AD theories in terms of the topology of the corresponding 3D mirror quivers, and we show that our isolated AD theories generically admit renormalization group (RG) ows to interacting superconformal eld theories (SCFTs) with thirty-two (Poincar e plus special) supercharges. Motivated by these examples, we argue that, in a sense we make precise, the existence of RG ows to interacting SCFTs with thirty-two supercharges is generic in a far larger class of 4D N = 2 SCFTs arising from compacti cations of the 6D (2; 0) theory on surfaces with irregular singularities. Then we study fusion rules in modular tensor categories. We rst relate fusion rules to the mathematical conjecture of Arad and Herzog (AH) in group theory: in nite simple groups, the product of two conjugacy classes of length greater than one is never a single conjugacy class. We discuss implications of this conjecture for nonabelian anyons in 2 + 1-dimensional discrete gauge theories. Thinking in this way suggests closely related statements about nite simple groups and their associated discrete gauge theories. We prove these statements and give physical intuition for their validity. Finally, we explain that the lack of certain dualities in theories with nonabelian nite simple gauge groups provides a non-trivial check of the AH conjecture. We also study the implications of the anyon fusion equation a b = c on global properties of 2 + 1D topological quantum eld theories (TQFTs). Here a and b are anyons that fuse together to give a unique anyon, c. As is well known, when at least one of a and b is abelian, such equations describe aspects of the one-form symmetry of the theory. When a and b are non-abelian, the most obvious way such fusions arise is when a TQFT can be resolved into a product of TQFTs with trivial mutual braiding, and a and b lie in separate factors. More generally, we argue that the appearance of such fusions for non-abelian a and b can also be an indication of zero-form symmetries in a TQFT, of what we term \quasi-zero-form symmetries" (as in the case of discrete gauge 1Chapter two, three , four are based on the papers [34],[35],[36] respectively. 2 theories based on the largest Mathieu group, M24), or of the existence of non-modular fusion subcategories. We study these ideas in a variety of TQFT settings from (twisted and untwisted) discrete gauge theories to Chern-Simons theories based on continuous gauge groups and related cosets. Along the way, we prove various useful theorems

On Classification of Fermionic Rational Conformal Field Theories

We systematically study how the integrality of the conformal characters shapes the space of fermionic rational conformal field theories in two dimensions. The integrality suggests that conformal characters on torus with a given choice of spin structures should be invariant under a principal congruence subgroup of $\mathrm{PSL}(2,\mathbb{Z})$. The invariance strongly constrains the possible values of the central charge as well as the conformal weights in both Neveu-Schwarz and Ramond sectors, which improves the conventional holomorphic modular bootstrap method in a significant manner. This allows us to make much progress on the classification of fermionic rational conformal field theories with the number of independent characters less than five.Comment: 36 pages, 1 figure; minor changes, published versio

EFFECT OF HARVESTING QUOTA AND PROTECTION ZONE IN A REACTION-DIFFUSION MODEL ARISING FROM FISHERY MANAGEMENT

A reaction-diffusion logistic population model with spatially nonhomogeneous harvesting is considered. It is shown that when the intrinsic growth rate is larger than the principal eigenvalue of the protection zone, then the population is always sustainable; while in the opposite case, there exists a maximum allowable catch to avoid the population extinction. The existence of steady state solutions is also studied for both cases. The existence of an optimal harvesting pattern is also shown, and theoretical results are complemented by some numerical simulations for one-dimensional domains

Modeling relation paths for knowledge base completion via joint adversarial training

Knowledge Base Completion (KBC), which aims at determining the missing relations between entity pairs, has received increasing attention in recent years. Most existing KBC methods focus on either embedding the Knowledge Base (KB) into a specific semantic space or leveraging the joint probability of Random Walks (RWs) on multi-hop paths. Only a few unified models take both semantic and path-related features into consideration with adequacy. In this paper, we propose a novel method to explore the intrinsic relationship between the single relation (i.e. 1-hop path) and multi-hop paths between paired entities. We use Hierarchical Attention Networks (HANs) to select important relations in multi-hop paths and encode them into low-dimensional vectors. By treating relations and multi-hop paths as two different input sources, we use a feature extractor, which is shared by two downstream components (i.e. relation classifier and source discriminator), to capture shared/similar information between them. By joint adversarial training, we encourage our model to extract features from the multi-hop paths which are representative for relation completion. We apply the trained model (except for the source discriminator) to several large-scale KBs for relation completion. Experimental results show that our method outperforms existing path information-based approaches. Since each sub-module of our model can be well interpreted, our model can be applied to a large number of relation learning tasks.Comment: Accepted by Knowledge-Based System