1,120 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Bayesian Nonlinear Tensor Regression with Functional Fused Elastic Net Prior

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    Tensor regression methods have been widely used to predict a scalar response from covariates in the form of a multiway array. In many applications, the regions of tensor covariates used for prediction are often spatially connected with unknown shapes and discontinuous jumps on the boundaries. Moreover, the relationship between the response and the tensor covariates can be nonlinear. In this article, we develop a nonlinear Bayesian tensor additive regression model to accommodate such spatial structure. A functional fused elastic net prior is proposed over the additive component functions to comprehensively model the nonlinearity and spatial smoothness, detect the discontinuous jumps, and simultaneously identify the active regions. The great flexibility and interpretability of the proposed method against the alternatives are demonstrated by a simulation study and an analysis on facial feature data

    Thematic Working Group 3 - Inclusion of Excluded Populations : Access and Learning Optimization via IT in the Post-Pandemic Era

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    Thematic Working Group (TWG) 3’s theme is “Inclusion of excluded populations: access and learning optimization via IT in the post-pandemic era”. A focal concern is established by the presence of the first word – ‘inclusion’ – and how this relates to ‘excluded populations’. Much of the research in this field has focused on inclusion for individuals; however, the evidence shows that educational exclusion has multiple dimensions (Passey, 2014). To accommodate this within the current focus, therefore, identifying key dimensions of ‘excluded populations’ will be a key concern of this document. ‘Access’ will be considered beyond physical technology access, involving aspects of accessibility, agency and empowerment. These aspects relate to a definition of access that concerns the needs for individuals to develop and have digital capabilities and abilities to select applications appropriate to purpose, as discussed, for example, by Helsper (2021) and Passey et al. (2018). Taking this wider concern for access, ‘learning optimization’ will be explored as a term that highlights the need to focus on technological access and provision enabling successful outcomes. Given the fact that the intention of the work of TWG3 is to explore findings in the ‘post-pandemic’ context, communication technologies as well as just information technology, ‘IT’, are clearly important and need to be considered. Additionally, exclusion factors to be addressed need to be clearly identified so that inclusion can be accommodated and ensured in the context of specific excluded populations. However, inclusion should not be implemented as an imposition in the context of digital technologies, as some populations do not wish to use digital technologies (Wetmore, 2007), and in this respect the issue of the need to acknowledge diversity is important

    Data Mining in Internet of Things Systems: A Literature Review

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    The Internet of Things (IoT) and cloud technologies have been the main focus of recent research, allowing for the accumulation of a vast amount of data generated from this diverse environment. These data include without any doubt priceless knowledge if could correctly discovered and correlated in an efficient manner. Data mining algorithms can be applied to the Internet of Things (IoT) to extract hidden information from the massive amounts of data that are generated by IoT and are thought to have high business value. In this paper, the most important data mining approaches covering classification, clustering, association analysis, time series analysis, and outlier analysis from the knowledge will be covered. Additionally, a survey of recent work in in this direction is included. Another significant challenges in the field are collecting, storing, and managing the large number of devices along with their associated features. In this paper, a deep look on the data mining for the IoT platforms will be given concentrating on real applications found in the literatur

    Single-Exponential FPT Algorithms for Enumerating Secluded F\mathcal{F}-Free Subgraphs and Deleting to Scattered Graph Classes

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    The celebrated notion of important separators bounds the number of small (S,T)(S,T)-separators in a graph which are 'farthest from SS' in a technical sense. In this paper, we introduce a generalization of this powerful algorithmic primitive that is phrased in terms of kk-secluded vertex sets: sets with an open neighborhood of size at most kk. In this terminology, the bound on important separators says that there are at most 4k4^k maximal kk-secluded connected vertex sets CC containing SS but disjoint from TT. We generalize this statement significantly: even when we demand that G[C]G[C] avoids a finite set F\mathcal{F} of forbidden induced subgraphs, the number of such maximal subgraphs is 2O(k)2^{O(k)} and they can be enumerated efficiently. This allows us to make significant improvements for two problems from the literature. Our first application concerns the 'Connected kk-Secluded F\mathcal{F}-free subgraph' problem, where F\mathcal{F} is a finite set of forbidden induced subgraphs. Given a graph in which each vertex has a positive integer weight, the problem asks to find a maximum-weight connected kk-secluded vertex set C⊆V(G)C \subseteq V(G) such that G[C]G[C] does not contain an induced subgraph isomorphic to any F∈FF \in \mathcal{F}. The parameterization by kk is known to be solvable in triple-exponential time via the technique of recursive understanding, which we improve to single-exponential. Our second application concerns the deletion problem to scattered graph classes. Here, the task is to find a vertex set of size at most kk whose removal yields a graph whose each connected component belongs to one of the prescribed graph classes Π1,
,Πd\Pi_1, \ldots, \Pi_d. We obtain a single-exponential algorithm whenever each class Πi\Pi_i is characterized by a finite number of forbidden induced subgraphs. This generalizes and improves upon earlier results in the literature.Comment: To appear at ISAAC'2

    Investigating Digital Corporate Reporting from an Upper Echelons Theory Perspective: Evidence from the Arab Middle East

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    Utilising the insights of Upper Echelons Theory (UET) and bounded rationality assumption, this original study aimed to investigate the association between corporate leaders’ characteristics and both the extent and readability of Digital Corporate Reporting (DCR). Content analysis of corporate websites of 122 publicly listed Jordananian firms has been carried out. The logistics regression analysis revealed that maintaining a functioning corporate website is inversely associated with CEO age. This indicates that younger CEOs are more likely to retain a web presence for the firm than their older counterparts. The OLS regression analysis revealed that CEOs’ education and tenure were negatively associated with the extent of DCR. Moreover, it was found that Corporate Governance (CG) moderating variables hardly lessen this relationship. The results confirm the current thoughts regarding the rise of CEO effects in corporations with unique evidence from the Arab Middle East (AME). Building on the previous evidence, the study also aimed at uncovering the association between chairman characteristics and the readability of the digital version of the chairman’s Letter to Shareholders (LTS). A hand-built dataset from a sample of 379 LTS from 101 publicly listed firms in 7 AME countries over five years (2014 – 2018) were employed to achieve this objective. Focusing on the clarity of DCR, the results of this second part of this study emphasizes the potential of UET to provide incremental plausible explanations of the variance in the levels of readability of LTS. The clustered regression results of the panel data demonstrate that older and less educated chairpersons are associated with more readable disclosures. Such findings on disclosure styles demonstrate the power of individuals in positions of authority as a consequence of higher education and tenure. Such findings contribute to the evolving inquiry on the significance of readability for enhancing corporate disclosure transparency and have implications for improving the DCR extent and readability

    A unified framework for Simplicial Kuramoto models

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    Simplicial Kuramoto models have emerged as a diverse and intriguing class of models describing oscillators on simplices rather than nodes. In this paper, we present a unified framework to describe different variants of these models, categorized into three main groups: "simple" models, "Hodge-coupled" models, and "order-coupled" (Dirac) models. Our framework is based on topology, discrete differential geometry as well as gradient flows and frustrations, and permits a systematic analysis of their properties. We establish an equivalence between the simple simplicial Kuramoto model and the standard Kuramoto model on pairwise networks under the condition of manifoldness of the simplicial complex. Then, starting from simple models, we describe the notion of simplicial synchronization and derive bounds on the coupling strength necessary or sufficient for achieving it. For some variants, we generalize these results and provide new ones, such as the controllability of equilibrium solutions. Finally, we explore a potential application in the reconstruction of brain functional connectivity from structural connectomes and find that simple edge-based Kuramoto models perform competitively or even outperform complex extensions of node-based models.Comment: 36 pages, 11 figure

    Network Intrusion Detection with Edge-Directed Graph Multi-Head Attention Networks

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    A network intrusion usually involves a number of network locations. Data flow (including the data generated by intrusion behaviors) among these locations (usually represented by IP addresses) naturally forms a graph. Thus, graph neural networks (GNNs) have been used in the construction of intrusion detection models in recent years since they have an excellent ability to capture graph topological features of intrusion data flow. However, existing GNN models treat node mean aggregation equally in node information aggregation. In reality, the correlations of nodes and their neighbors as well as the linked edges are different. Assigning higher weights to nodes and edges with high similarity can highlight the correlation among them, which will enhance the accuracy and expressiveness of the model. To this end, this paper proposes novel Edge-Directed Graph Multi-Head Attention Networks (EDGMAT) for network intrusion detection. The proposed EDGMAT model introduces a multi-head attention mechanism into the intrusion detection model. Additional weight learning is realized through the combination of a multi-head attention mechanism and edge features. Weighted aggregation makes better use of the relationship between different network traffic data. Experimental results on four recent NIDS benchmark datasets show that the performance of EDGMAT in terms of weighted F1-Score is significantly better than that of four state-of-the-art models in multi-class detection tasks

    Parameterized Graph Modification Beyond the Natural Parameter

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    Spectral Ranking Inferences based on General Multiway Comparisons

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    This paper studies the performance of the spectral method in the estimation and uncertainty quantification of the unobserved preference scores of compared entities in a very general and more realistic setup in which the comparison graph consists of hyper-edges of possible heterogeneous sizes and the number of comparisons can be as low as one for a given hyper-edge. Such a setting is pervasive in real applications, circumventing the need to specify the graph randomness and the restrictive homogeneous sampling assumption imposed in the commonly-used Bradley-Terry-Luce (BTL) or Plackett-Luce (PL) models. Furthermore, in the scenarios when the BTL or PL models are appropriate, we unravel the relationship between the spectral estimator and the Maximum Likelihood Estimator (MLE). We discover that a two-step spectral method, where we apply the optimal weighting estimated from the equal weighting vanilla spectral method, can achieve the same asymptotic efficiency as the MLE. Given the asymptotic distributions of the estimated preference scores, we also introduce a comprehensive framework to carry out both one-sample and two-sample ranking inferences, applicable to both fixed and random graph settings. It is noteworthy that it is the first time effective two-sample rank testing methods are proposed. Finally, we substantiate our findings via comprehensive numerical simulations and subsequently apply our developed methodologies to perform statistical inferences on statistics journals and movie rankings
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