scholarly journals Robust minimal matching rules for quasicrystals

2019 ◽  
Vol 75 (5) ◽  
pp. 669-693 ◽  
Author(s):  
Pavel Kalugin ◽  
André Katz
Keyword(s):  
Real World ◽  
Diffraction Data ◽  
Spatial Density ◽  
Unified Framework ◽  
Minimal Set ◽  
Minimal Matching ◽  
Matching Rules

A unified framework is proposed for dealing with matching rules of quasiperiodic patterns, relevant for both tiling models and real-world quasicrystals. The approach is intended for extraction and validation of a minimal set of matching rules, directly from the phased diffraction data. The construction yields precise values for the spatial density of distinct atomic positions and tolerates the presence of defects in a robust way.

scholarly journals A Multi-Tiered Framework for Insider Threat Prevention

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1005
Author(s):  
Rakan A. Alsowail ◽  
Taher Al-Shehari
Keyword(s):  
Real World ◽  
Relevant Literature ◽  
Security And Privacy ◽  
Insider Threat ◽  
Unified Framework ◽  
Insider Threats ◽  
Public And Private ◽  
Private Organizations ◽  
Confidential Data ◽  
Real Value

As technologies are rapidly evolving and becoming a crucial part of our lives, security and privacy issues have been increasing significantly. Public and private organizations have highly confidential data, such as bank accounts, military and business secrets, etc. Currently, the competition between organizations is significantly higher than before, which triggers sensitive organizations to spend an excessive volume of their budget to keep their assets secured from potential threats. Insider threats are more dangerous than external ones, as insiders have a legitimate access to their organization’s assets. Thus, previous approaches focused on some individual factors to address insider threat problems (e.g., technical profiling), but a broader integrative perspective is needed. In this paper, we propose a unified framework that incorporates various factors of the insider threat context (technical, psychological, behavioral and cognitive). The framework is based on a multi-tiered approach that encompasses pre, in and post-countermeasures to address insider threats in an all-encompassing perspective. It considers multiple factors that surround the lifespan of insiders’ employment, from the pre-joining of insiders to an organization until after they leave. The framework is utilized on real-world insider threat cases. It is also compared with previous work to highlight how our framework extends and complements the existing frameworks. The real value of our framework is that it brings together the various aspects of insider threat problems based on real-world cases and relevant literature. This can therefore act as a platform for general understanding of insider threat problems, and pave the way to model a holistic insider threat prevention system.

Causal Feature Selection with Missing Data

2022 ◽  
Vol 16 (4) ◽  
pp. 1-24
Author(s):  
Kui Yu ◽  
Yajing Yang ◽  
Wei Ding
Keyword(s):  
Feature Selection ◽  
Missing Data ◽  
Real World ◽  
Missing Values ◽  
Prediction Models ◽  
Causal Structure ◽  
Data Imputation ◽  
Accurate Data ◽  
Unified Framework ◽  
Class Variable

Causal feature selection aims at learning the Markov blanket (MB) of a class variable for feature selection. The MB of a class variable implies the local causal structure among the class variable and its MB and all other features are probabilistically independent of the class variable conditioning on its MB, this enables causal feature selection to identify potential causal features for feature selection for building robust and physically meaningful prediction models. Missing data, ubiquitous in many real-world applications, remain an open research problem in causal feature selection due to its technical complexity. In this article, we discuss a novel multiple imputation MB (MimMB) framework for causal feature selection with missing data. MimMB integrates Data Imputation with MB Learning in a unified framework to enable the two key components to engage with each other. MB Learning enables Data Imputation in a potentially causal feature space for achieving accurate data imputation, while accurate Data Imputation helps MB Learning identify a reliable MB of the class variable in turn. Then, we further design an enhanced kNN estimator for imputing missing values and instantiate the MimMB. In our comprehensively experimental evaluation, our new approach can effectively learn the MB of a given variable in a Bayesian network and outperforms other rival algorithms using synthetic and real-world datasets.

scholarly journals Unsupervised Learning for Large Scale Data: The ATHLOS Project

2021 ◽  
Author(s):  
Petros Barmpas ◽  
Sotiris Tasoulis ◽  
Aristidis G. Vrahatis ◽  
Panagiotis Anagnostou ◽  
Spiros Georgakopoulos ◽  
...  
Keyword(s):  
Unsupervised Learning ◽  
Real World ◽  
Large Scale ◽  
High Dimensional Data ◽  
Experimental Studies ◽  
Mixed Data ◽  
Categorical Variables ◽  
High Dimensional ◽  
Data Types ◽  
Unified Framework

1AbstractRecent technological advancements in various domains, such as the biomedical and health, offer a plethora of big data for analysis. Part of this data pool is the experimental studies that record various and several features for each instance. It creates datasets having very high dimensionality with mixed data types, with both numerical and categorical variables. On the other hand, unsupervised learning has shown to be able to assist in high-dimensional data, allowing the discovery of unknown patterns through clustering, visualization, dimensionality reduction, and in some cases, their combination. This work highlights unsupervised learning methodologies for large-scale, high-dimensional data, providing the potential of a unified framework that combines the knowledge retrieved from clustering and visualization. The main purpose is to uncover hidden patterns in a high-dimensional mixed dataset, which we achieve through our application in a complex, real-world dataset. The experimental analysis indicates the existence of notable information exposing the usefulness of the utilized methodological framework for similar high-dimensional and mixed, real-world applications.

Comprehensive Semi-Supervised Multi-Modal Learning

2019 ◽  
Author(s):  
Yang Yang ◽  
Ke-Tao Wang ◽  
De-Chuan Zhan ◽  
Hui Xiong ◽  
Yuan Jiang
Keyword(s):  
Real World ◽  
Regularization Method ◽  
Empirical Studies ◽  
Attention Mechanism ◽  
Unified Framework ◽  
Real World Data ◽  
World Data ◽  
Modal Data ◽  
Real World Applications ◽  
Critical Challenge

Multi-modal learning refers to the process of learning a precise model to represent the joint representations of different modalities. Despite its promise for multi-modal learning, the co-regularization method is based on the consistency principle with a sufficient assumption, which usually does not hold for real-world multi-modal data. Indeed, due to the modal insufficiency in real-world applications, there are divergences among heterogeneous modalities. This imposes a critical challenge for multi-modal learning. To this end, in this paper, we propose a novel Comprehensive Multi-Modal Learning (CMML) framework, which can strike a balance between the consistency and divergency modalities by considering the insufficiency in one unified framework. Specifically, we utilize an instance level attention mechanism to weight the sufficiency for each instance on different modalities. Moreover, novel diversity regularization and robust consistency metrics are designed for discovering insufficient modalities. Our empirical studies show the superior performances of CMML on real-world data in terms of various criteria.

scholarly journals Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction

2019 ◽  
Vol 33 ◽  
pp. 5668-5675 ◽  
Author(s):  
Huaxiu Yao ◽  
Xianfeng Tang ◽  
Hua Wei ◽  
Guanjie Zheng ◽  
Zhenhui Li
Keyword(s):  
Real World ◽  
Spatial Dependence ◽  
Large Scale ◽  
Temporal Dynamics ◽  
Dynamic Network ◽  
Research Field ◽  
Traffic Prediction ◽  
Unified Framework ◽  
Gating Mechanism ◽  
Spatial Dependencies

Traffic prediction has drawn increasing attention in AI research field due to the increasing availability of large-scale traffic data and its importance in the real world. For example, an accurate taxi demand prediction can assist taxi companies in pre-allocating taxis. The key challenge of traffic prediction lies in how to model the complex spatial dependencies and temporal dynamics. Although both factors have been considered in modeling, existing works make strong assumptions about spatial dependence and temporal dynamics, i.e., spatial dependence is stationary in time, and temporal dynamics is strictly periodical. However, in practice the spatial dependence could be dynamic (i.e., changing from time to time), and the temporal dynamics could have some perturbation from one period to another period. In this paper, we make two important observations: (1) the spatial dependencies between locations are dynamic; and (2) the temporal dependency follows daily and weekly pattern but it is not strictly periodic for its dynamic temporal shifting. To address these two issues, we propose a novel Spatial-Temporal Dynamic Network (STDN), in which a flow gating mechanism is introduced to learn the dynamic similarity between locations, and a periodically shifted attention mechanism is designed to handle long-term periodic temporal shifting. To the best of our knowledge, this is the first work that tackle both issues in a unified framework. Our experimental results on real-world traffic datasets verify the effectiveness of the proposed method.

scholarly journals Semi-Supervised Multi-Modal Learning with Incomplete Modalities

2018 ◽  
Author(s):  
Yang Yang ◽  
De-Chuan Zhan ◽  
Xiang-Rong Sheng ◽  
Yuan Jiang
Keyword(s):  
Data Collection ◽  
Real World ◽  
Feature Representation ◽  
Learning Approaches ◽  
Learning Method ◽  
Extrinsic Information ◽  
Unified Framework ◽  
Multiple Modalities ◽  
Real World Applications ◽  
Training Examples

In real world applications, data are often with multiple modalities. Researchers proposed the multi-modal learning approaches for integrating the information from different modalities. Most of the previous multi-modal methods assume that training examples are with complete modalities. However, due to the failures of data collection, self-deficiencies and other various reasons, multi-modal examples are usually with incomplete feature representation in real applications. In this paper, the incomplete feature representation issues in multi-modal learning are named as incomplete modalities, and we propose a semi-supervised multi-modal learning method aimed at this incomplete modal issue (SLIM). SLIM can utilize the extrinsic information from unlabeled data against the insufficiencies brought by the incomplete modal issues in a semi-supervised scenario. Besides, the proposed SLIM forms the problem into a unified framework which can be treated as a classifier or clustering learner, and integrate the intrinsic consistencies and extrinsic unlabeled information. As SLIM can extract the most discriminative predictors for each modality, experiments on 15 real world multi-modal datasets validate the effectiveness of our method.

scholarly journals Self-Guided Community Detection on Networks with Missing Edges

2021 ◽  
Author(s):  
Dongxiao He ◽  
Shuai Li ◽  
Di Jin ◽  
Pengfei Jiao ◽  
Yuxiao Huang
Keyword(s):  
Community Structure ◽  
Em Algorithm ◽  
Community Detection ◽  
Real World ◽  
Expectation Maximization ◽  
Link Prediction ◽  
Inference Method ◽  
Unified Framework ◽  
New Model ◽  
Detection Algorithms

The vast majority of community detection algorithms assume that the networks are totally observed. However, in reality many networks cannot be fully observed. On such network is edges-missing network, where some relationships (edges) between two entities are missing. Recently, several works have been proposed to solve this problem by combining link prediction and community detection in a two-stage method or in a unified framework. However, the goal of link prediction, which is to predict as many correct edges as possible, is not consistent with the requirement for predicting the important edges for discovering community structure on edges-missing networks. Thus, combining link prediction and community detection cannot work very well in terms of detecting community structure for edges-missing network. In this paper, we propose a community self-guided generative model which jointly completes the edges-missing network and identifies communities. In our new model, completing missing edges and identifying communities are not isolated but closely intertwined. Furthermore, we developed an effective model inference method that combines a nested Expectation-Maximization (EM) algorithm and Metropolis-Hastings Sampling. Extensive experiments on real-world edges-missing networks show that our model can effectively detect community structures while completing missing edges.

scholarly journals Deep Robust Unsupervised Multi-Modal Network

2019 ◽  
Vol 33 ◽  
pp. 5652-5659
Author(s):  
Yang Yang ◽  
Yi-Feng Wu ◽  
De-Chuan Zhan ◽  
Zhi-Bin Liu ◽  
Yuan Jiang
Keyword(s):  
Real World ◽  
Feature Learning ◽  
Learning Performance ◽  
Real Problem ◽  
Learning Approaches ◽  
Unified Framework ◽  
Heterogeneous Information ◽  
Feature Representations ◽  
Discriminative Feature ◽  
Different Sources

In real-world applications, data are often with multiple modalities, and many multi-modal learning approaches are proposed for integrating the information from different sources. Most of the previous multi-modal methods utilize the modal consistency to reduce the complexity of the learning problem, therefore the modal completeness needs to be guaranteed. However, due to the data collection failures, self-deficiencies, and other various reasons, multi-modal instances are often incomplete in real applications, and have the inconsistent anomalies even in the complete instances, which jointly result in the inconsistent problem. These degenerate the multi-modal feature learning performance, and will finally affect the generalization abilities in different tasks. In this paper, we propose a novel Deep Robust Unsupervised Multi-modal Network structure (DRUMN) for solving this real problem within a unified framework. The proposed DRUMN can utilize the extrinsic heterogeneous information from unlabeled data against the insufficiency caused by the incompleteness. On the other hand, the inconsistent anomaly issue is solved with an adaptive weighted estimation, rather than adjusting the complex thresholds. As DRUMN can extract the discriminative feature representations for each modality, experiments on real-world multimodal datasets successfully validate the effectiveness of our proposed method.

scholarly journals A Unified Framework for Knowledge Intensive Gradient Boosting: Leveraging Human Experts for Noisy Sparse Domains

2020 ◽  
Vol 34 (04) ◽  
pp. 4460-4468
Author(s):  
Harsha Kokel ◽  
Phillip Odom ◽  
Shuo Yang ◽  
Sriraam Natarajan
Keyword(s):  
Real World ◽  
Domain Knowledge ◽  
Gradient Boosting ◽  
Unified Framework ◽  
Classification And Regression ◽  
Knowledge Intensive

Incorporating richer human inputs including qualitative constraints such as monotonic and synergistic influences has long been adapted inside AI. Inspired by this, we consider the problem of using such influence statements in the successful gradient-boosting framework. We develop a unified framework for both classification and regression settings that can both effectively and efficiently incorporate such constraints to accelerate learning to a better model. Our results in a large number of standard domains and two particularly novel real-world domains demonstrate the superiority of using domain knowledge rather than treating the human as a mere labeler.

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