Reviewerly/exHarmony huggingface dataset:
Fake Node Attacks on Graph Convolutional Networks

In this paper, we study the robustness of graph convolutional networks (GCNs). Previous works have shown that GCNs are vulnerable to adversarial perturbation on adjacency or feature matrices of existing nodes; however, such attacks are usually unrealistic in real applications. For instance, in social network applications, the attacker will need to hack into either the client or server to change existing links or features. In this paper, we propose a new type of “fake node attacks” to attack GCNs by adding malicious fake nodes. This is much more realistic than previous attacks; in social network applications, the attacker only needs to register a set of fake accounts and link to existing ones. To conduct fake node attacks, a greedy algorithm is proposed to generate edges of malicious nodes and their corresponding features aiming to minimize the classification accuracy on the target nodes. In addition, we introduce a discriminator to classify malicious nodes from real nodes and propose a Greedy-generative adversarial network attack to simultaneously update the discriminator and the attacker, to make malicious nodes indistinguishable from the real ones. Our non-targeted attack decreases the accuracy of GCN down to 0.03, and our targeted attack reaches a success rate of 78% on a group of 100 nodes and 90% on average for attacking a single target node.
Learning Implicit Feature Alignment Function for Semantic Segmentation

Integrating high-level context information with low-level details is of central importance in semantic segmentation. Towards this end, most existing segmentation models apply bilinear up-sampling and convolutions to feature maps of different scales, and then align them at the same resolution. However, bilinear up-sampling blurs the precise information learned in these feature maps and convolutions incur extra computation costs. To address these issues, we propose the Implicit Feature Alignment function (IFA). Our method is inspired by the rapidly expanding topic of implicit neural representations, where coordinate-based neural networks are used to designate fields of signals. In IFA, feature vectors are viewed as representing a 2D field of information. Given a query coordinate, nearby feature vectors with their relative coordinates are taken from the multi-level feature maps and then fed into an MLP to generate the corresponding output. As such, IFA implicitly aligns the feature maps at different levels and is capable of producing segmentation maps in arbitrary resolutions. We demonstrate the efficacy of IFA on multiple datasets, including Cityscapes, PASCAL Context, and ADE20K. Our method can be combined with improvement on various architectures, and it achieves state-of-the-art computation-accuracy trade-off on common benchmarks. Code is available at https://github.com/hzhupku/IFA .
Where is IT in Information Security? The Interrelationship among IT Investment, Security Awareness, and Data Breaches

Data breaches can severely damage a firm’s reputation and its customers’ confidence. Firms must therefore continuously invest in security measures to prevent such breaches. However, the effectiveness of security investment has been questioned by both practitioners and academics. We illustrate the bidirectional dynamic relationship between information technology (IT) investment and data breaches moderated by threat and countermeasure security awareness using an eight-year panel of 311 U.S.-listed firms to provide empirical evidence that threat awareness broadens firms’ scope for addressing data-breach issues by investing more in IT than in security. Countermeasure awareness equips firms with sufficient knowledge and experience to ensure effective implementation of IT, which provides more comprehensive protection than security investment alone. Our results suggest that firms should evolve beyond the reactive mindset of solely upgrading security and begin nurturing both threat awareness and countermeasure awareness to address the underlying IT system issues that are the cause of data breaches.
Déjàvu Project as a digital solution to help the appraisal of documents focused on digital humanities

This article aims to analyze the DéjàVu Project as a complementary solution to the archival function of document appraisal in the digital humanities. It is exploratory and qualitative research with technical procedures related to the bibliographic study of digital humanities and documents. The digital solution refers to the combination of digital activities of data collection and cleaning, followed by organization and synchronization for later understanding and inference in various fields, including appraisal support. We concluded that using the solution developed in the DéjàVu Project has the potential to be used complementary to document appraisal. It provides a context substrate for a more accurate, pluralistic, and representative appraisal of social demands. It primarily facilitates the attribution of social values in the Macro-Appraisal process.
Artificial Intelligence

As we navigate the digital era of the 21st century, cyber security has grown into a pressing societal issue that requires innovative, cutting-edge solutions. In response to this pressing need, Artificial Intelligence (AI) has emerged as a revolutionary instrument, causing a paradigm shift in cyber security. AI's prowess resides in its capacity to process and analyze immense quantities of heterogeneous cyber security data, thereby facilitating the efficient completion of crucial tasks. These duties, which include threat detection, asset prioritization, and vulnerability management, are performed with a level of speed and accuracy that far exceeds human capabilities, thereby transforming our approach to cyber security. This document provides a comprehensive dissection of AI's profound impact on cyber security, as well as an in-depth analysis of how AI tools not only augment, but in many cases transcend human-mediated processes. By delving into the complexities of AI implementation within the realm of cyber security, we demonstrate the potential for AI to effectively anticipate, identify, and preempt cyber threats, empowering organizations to take a proactive stance towards digital safety. Despite these advancements, it is essential to consider the inherent limitations of AI. We emphasize the need for sustained human oversight and intervention to ensure that cyber security measures are proportionate and effective. Importantly, we address potential ethical concerns and emphasize the significance of robust governance structures for the responsible and transparent use of artificial intelligence in cyber security. This paper clarifies the transformative role of AI in reshaping cyber security strategies, thereby contributing to a safer, more secure digital future. In doing so, it sets the groundwork for further exploration and discussion on the use of AI in cyber security, a discussion that is becoming increasingly important as we continue to move deeper into the digital age.
UEcho: A Model of Uncertainty Management in Human Abductive Reasoning

This paper explores the uncertainty aspects of human abductive reasoning. Echo, a model of abduction based on the Theory of Explanatory Coherence (Thagard, 1992a), captures many aspects of human abductive reasoning, but fails to sufficiently manage the uncertainty in abduction. In particular, Echo dons not handle belief acquisition and dynamic belief revision, two essential components of human abductive reasoning. We propose a modified Echo model (UEcho), in which we add a learning mechanism for belief acquisition and a dynamic processing mechanism for belief revision. To evaluate the model, we report an empirical study in which base rate learning serves as a testbed for belief acquisition and the order effect serves as a testbed for belief revision.
Automatic Generation of Typographic Font From Small Font Subset

The automated generation of fonts containing a large number of characters is in high demand. For example, a typical Japanese font requires over 1000 characters. Unfortunately, professional typographers create the majority of fonts, resulting in significant financial and time investments for font generation. The main contribution of this article is the development of a method that automatically generates a target typographic font containing thousands of characters, from a small subset of character images in the target font. We generate characters other than the subset so that a complete font is obtained. We propose a novel font generation method with the capability to deal with various fonts, including a font composed of distinctive strokes, which are difficult for existing methods to handle. We demonstrated the proposed method by generating 2965 characters in 47 fonts. Moreover, objective and subjective evaluations verified that the generated characters are similar to the original characters.
Conditional Quantum One-Time Pad

Suppose that Alice and Bob are located in distant laboratories, which are connected by an ideal quantum channel. Suppose further that they share many copies of a quantum state $\rho_{ABE}$, such that Alice possesses the $A$ systems and Bob the $BE$ systems. In our model, there is an identifiable part of Bob's laboratory that is insecure: a third party named Eve has infiltrated Bob's laboratory and gained control of the $E$ systems. Alice, knowing this, would like use their shared state and the ideal quantum channel to communicate a message in such a way that Bob, who has access to the whole of his laboratory ($BE$ systems), can decode it, while Eve, who has access only to a sector of Bob's laboratory ($E$ systems) and the ideal quantum channel connecting Alice to Bob, cannot learn anything about Alice's transmitted message. We call this task the conditional one-time pad, and in this paper, we prove that the optimal rate of secret communication for this task is equal to the conditional quantum mutual information $I(A;B|E)$ of their shared state. We thus give the conditional quantum mutual information an operational meaning that is different from those given in prior works, via state redistribution, conditional erasure, or state deconstruction. We also generalize the model and method in several ways, one of which demonstrates that the negative tripartite interaction information $-I_{3}(A;B;E) = I(A;BE)-I(A;B)-I(A;E)$ of a tripartite state $\rho_{ABE}$ is an achievable rate for a secret-sharing task, i.e., the case in which Alice's message should be secure from someone possessing only the $AB$ or $AE$ systems but should be decodable by someone possessing all systems $A$, $B$, and $E$.
Integrating educational theories into a feasible digital environment

Nowadays there is a tremendous effort to enhance educational procedure with the incorporation of new technologies aiming at the better understanding of educational material from students. In this direction many theories have been developed, some of which have been implemented into digital systems. A challenge here is to develop a system that integrates more than one of such theories in a harmonic way for the benefit of student community. In this work we attempt to give an answer to this challenge proposing a novel system that integrates the main characteristics of three well-known educational theories: learning objects, collaborative learning and mobile learning in the context of cultural heritage teaching and learning. Evaluation results suggest that such an attempt is technically feasible, the integrated characteristics of the educational theories remain visible to educators of cultural heritage courses, while cultural heritage educators positively accept the potential incorporation of the platform in the educational procedure.
Approximate stabilizer rank and improved weak simulation of Clifford-dominated circuits for qudits

Bravyi and Gosset recently gave classical simulation algorithms for quantum circuits dominated by Clifford operations. These algorithms scale exponentially with the number of T-gate in the circuit, but polynomially in the number of qubits and Clifford operations. Here we extend their algorithm to qudits of odd prime dimensions. We generalize their approximate stabilizer rank method for weak simulation to qudits and obtain the scaling of the approximate stabilizer rank with the number of single-qudit magic states. We also relate the canonical form of qudit stabilizer states to Gauss sum evaluations. We give an O(n^3) algorithm to calculating the inner product of two n-qudit stabilizer states.
Anomaly detection in scientific data using joint statistical moments

We propose an anomaly detection method for multi-variate scientific data based on analysis of high-order joint moments. Using kurtosis as a reliable measure of outliers, we suggest that principal kurtosis vectors, by analogy to principal component analysis (PCA) vectors, signify the principal directions along which outliers appear. The inception of an anomaly, then, manifests as a change in the principal values and vectors of kurtosis. Obtaining the principal kurtosis vectors requires decomposing a fourth order joint cumulant tensor for which we use a simple, computationally less expensive approach that involves performing a singular value decomposition (SVD) over the matricized tensor. We demonstrate the efficacy of this approach on synthetic data, and develop an algorithm to identify the occurrence of a spatial and/or temporal anomalous event in scientific phenomena. The algorithm decomposes the data into several spatial sub-domains and time steps to identify regions with such events. Feature moment metrics, based on the alignments of the principal kurtosis vectors, are computed at each sub-domain and time step for all features to quantify their relative importance towards the overall kurtosis in the data. Accordingly, spatial and temporal anomaly metrics for each sub-domain are proposed using the Hellinger distance of the feature moment metric distribution from a suitable nominal distribution. We apply the algorithm to two turbulent auto-ignition combustion cases and demonstrate that the anomaly metrics reliably capture the occurrence of auto-ignition in relevant spatial sub-domains at the right time steps.
Continuous phase spaces and the time evolution of spins: star products and spin-weighted spherical harmonics

We study continuous phase spaces of single spins and develop a complete description of their time evolution. The time evolution is completely specified by so-called star products. We explicitly determine these star products for general spin numbers using a simplified approach which applies spin-weighted spherical harmonics. This approach naturally relates phase spaces of increasing spin number to their quantum-optical limit and allows for efficient approximations of the time evolution for large spin numbers. We also approximate phase-space representations of certain quantum states that are challenging to calculate for large spin numbers. All of these applications are explored in concrete examples and we outline extensions to coupled spin systems.
Exploring Time-Varying Multivariate Volume Data Using Matrix of Isosurface Similarity Maps

We present a novel visual representation and interface named the matrix of isosurface similarity maps (MISM) for effective exploration of large time-varying multivariate volumetric data sets. MISM synthesizes three types of similarity maps (i.e., self, temporal, and variable similarity maps) to capture the essential relationships among isosurfaces of different variables and time steps. Additionally, it serves as the main visual mapping and navigation tool for examining the vast number of isosurfaces and exploring the underlying time-varying multivariate data set. We present temporal clustering, variable grouping, and interactive filtering to reduce the huge exploration space of MISM. In conjunction with the isovalue and isosurface views, MISM allows users to identify important isosurfaces or isosurface pairs and compare them over space, time, and value range. More importantly, we introduce path recommendation that suggests, animates, and compares traversal paths for effectively exploring MISM under varied criteria and at different levels-of-detail. A silhouette-based method is applied to render multiple surfaces of interest in a visually succinct manner. We demonstrate the effectiveness of our approach with case studies of several time-varying multivariate data sets and an ensemble data set, and evaluate our work with two domain experts.
<i>Homo prostheticus</i>? Intercorporeality and the emerging adult-smartphone assemblage

Purpose Young people’s attachment to their smartphones is well-documented, with smartphones often described as prostheses. While prior studies typically assume a clear human/machine divide, this paper aims to build on posthuman perspectives, exploring intercorporeality, the blurring of human/technology boundaries, between emerging adults and their smartphones. The paper aims to discuss these issues. Design/methodology/approach Drawing on assemblage theory, this interpretive study uses smartphone diaries and friendship pair/small group discussions with 27 British emerging adults. Findings Participants in this study are characterized as homo prostheticus , living with and through their phones, treating them as extensions of their mind and part of their selves as they navigated between their online and offline, private and social lives. Homo prostheticus was part of a broader assemblage or amalgamation of human and non-human components. As these components interacted with each other, the assemblage could be strengthened or weakened by various technological, personal and social factors. Research limitations/implications These qualitative findings are based on a particular sample at a particular point in time, within a particular culture. Further research could explore intercorporeality in human–smartphone relationships among other groups, in other cultures. Originality/value Although other studies have used prosthetic metaphors, this paper contributes to understanding of smartphones as a prostheses in the lives of emerging adults, highlighting intercorporeality as a key feature of homo prostheticus . It also uses assemblage theory to contextualize homo prostheticus and explores factors strengthening or weakening the broader human–smartphone assemblage.
Fast construction of correcting ensembles for legacy Artificial Intelligence systems: Algorithms and a case study

This paper presents a new approach for constructing simple and computationally efficient improvements of generic Artificial Intelligence (AI) systems, including Multilayer and Deep Learning neural networks. The improvements are small network ensembles added to the existing AI architectures. Theoretical foundations of the approach are based on stochastic separation theorems and the ideas of the concentration of measure. We show that, subject to mild technical assumptions on statistical properties of internal signals in the original AI system, the approach enables fast removal of the AI’s errors with probability close to one on the datasets which may be exponentially large in dimension. The approach is illustrated with numerical examples and a case study of digits recognition in American Sign Language.
Do employees in a “good” company comply better with information security policy? A corporate social responsibility perspective

Purpose The purpose of this paper is to investigate the impact of corporate social responsibility (CSR) on employees’ compliance behavior concerning information security policy (ISP). A research model includes CSR activities as an antecedent of ISP compliance and as a mediator of the relationship between ISP compliance intention and the perceived costs of compliance. Design/methodology/approach In total, 162 respondents were surveyed from organizations with more than 500 employees. This study used partial least squares (SmartPLS 3.0) to analyze and examine hypotheses. Findings The results show CSR’s influence as a mediator in the context of ISP compliance. In particular, moral CSR can affect employees’ ISP compliance intention positively and fully mediate the relationship between the costs of compliance and ISP compliance intention. Employees would like to comply with ISP when they recognize the benefits of ISP compliance and the costs of ISP noncompliance. Originality/value This study examines influential factors on ISP compliance considering cost-benefit factors from rational choice theory. Moreover, the study contributes to ISP compliance research by being the first attempt to consider CSR in an ISP compliance research context. The results provide insights on how to strategically implement CSR activities in terms of organizational information security.
General theory for stochastic admixture graphs and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e976" altimg="si3.gif"><mml:mi>F</mml:mi></mml:math>-statistics

We provide a general mathematical framework based on the theory of graphical models to study admixture graphs. Admixture graphs are used to describe the ancestral relationships between past and present populations, allowing for population merges and migration events, by means of gene flow. We give various mathematical properties of admixture graphs with particular focus on properties of the so-called F-statistics. Also the Wright–Fisher model is studied and a general expression for the loss of heterozygosity is derived.
Efficient Invisible and Unlinkable Sanitizable Signatures

Sanitizable signatures allow designated parties (the sanitizers) to apply arbitrary modifications to some restricted parts of signed messages. A secure scheme should not only be unforgeable, but also protect privacy and hold both the signer and the sanitizer accountable. Two important security properties that are seemingly difficult to achieve simultaneously and efficiently are invisibility and unlinkability. While invisibility ensures that the admissible modifications are hidden from external parties, unlinkability says that sanitized signatures cannot be linked to their sources. Achieving both properties simultaneously is crucial for applications where sensitive personal data is signed with respect to data-dependent admissible modifications. The existence of an efficient construction achieving both properties was recently posed as an open question by Camenisch et al. (PKC'17). In this work, we propose a solution to this problem with a two-step construction. First, we construct (non-accountable) invisible and unlinkable sanitizable signatures from signatures on equivalence classes and other basic primitives. Second, we put forth a generic transformation using verifiable ring signatures to turn any non-accountable sanitizable signature into an accountable one while preserving all other properties. When instantiating in the generic group and random oracle model, the efficiency of our construction is comparable to that of prior constructions, while providing stronger security guarantees.